human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
  Abstract: Massive MIMO is a variant of multiuser MIMO, where the number of antennas $M$ at the base-station is large, and generally much larger than the number of spatially multiplexed data streams to/from the users. It has been observed that in many realistic propagation scenarios as well as in spatially correlated channel models used in standardizations, although the user channel vectors have a very high-dim $M$, they lie on low-dim subspaces due to their limited angular spread. This low-dim subspace structure remains stable across many coherence blocks and can be exploited in several ways to improve the system performance. A main challenge, however, is to estimate this signal subspace from samples of users' channel vectors as fast and efficiently as possible. In a recent work, we addressed this problem and proposed a very effective novel algorithm referred to as Approximate Maximum-Likelihood (AML), which was formulated as a semi-definite program (SDP). In this paper, we address two problems left open in our previous work: computational complexity and tracking. The algorithm proposed in this paper is reminiscent of Multiple Measurement Vectors (MMV) problem in Compressed Sensing and is proved to be equivalent to the AML Algorithm for sufficiently dense angular grids. It has also a very low computational complexity and is able to track sharp transitions in the channel statistics very quickly. Although mainly motivated by massive MIMO applications, our proposed algorithm is of independent interest in other related subspace estimation applications. We assess the estimation/tracking performance of our proposed algorithm empirically via numerical simulations, especially in practically relevant situations where a direct implementation of the SDP would be infeasible in real-time. We also compare the performance of our algorithm with other related subspace estimation algorithms in the literature.

Connected through hyperedge HE-1 (induced by source paper "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems"):
  with members:
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "a generalized framework on beamformer design and csi acquisition for single carrier massive mimo systems in millimeter wave channels"
    - "hybrid beamforming for massive mimo a survey"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"

Connected through hyperedge HE-2 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-3 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "5g new radio unveiling the essentials of the next generation wireless access technology"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "structured channel covariance estimation from limited samples in massive mimo"):
  with members:
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "low complexity statistically robust precoder detector computation for massive mimo systems"

Connected through hyperedge HE-5 (induced by source paper "subspace tracking and least squares approaches to channel estimation in millimeter wave multiuser mimo"):
  with members:
    - "massive mimo in sub 6 ghz and mmwave physical practical and use case differences"
    - "cell free and user centric massive mimo at millimeter wave frequencies"
    - "subspace tracking algorithms for millimeter wave mimo channel estimation with hybrid beamforming"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"

Connected through hyperedge HE-6 (induced by source paper "improved scaling law for activity detection in massive mimo systems"):
  with members:
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a scalable and statistically robust beam alignment technique for mm wave systems"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "on the gap between rip properties and sparse recovery conditions"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-7 (induced by source paper "multi band covariance interpolation with applications in massive mimo"):
  with members:
    - "compressive estimation of a stochastic process with unknown autocorrelation function"
    - "low complexity and statistically robust beamformer design for massive mimo systems"
    - "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"
    - "truncated polynomial expansion downlink precoders and uplink detectors for massive mimo"
    - "channel vector subspace estimation from low dimensional projections"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "mimo downlink scheduling with non perfect channel state knowledge"

Connected through hyperedge HE-8 (induced by source paper "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"):
  with members:
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "cell free massive mimo uniformly great service for everyone"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)