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: "channel vector subspace estimation from low dimensional projections"
  Abstract: Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas $M$ is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal bandwidth of the order of 10 to 100 MHz, requires very large sampling bit-rate and power consumption. #R##N#In order to reduce such implementation requirements, Hybrid Digital-Analog architectures have been proposed. In particular, our work in this paper is motivated by one of such schemes named Joint Spatial Division and Multiplexing (JSDM), where the downlink precoder (resp., uplink linear receiver) is split into the product of a baseband linear projection (digital) and an RF reconfigurable beamforming network (analog), such that only a reduced number $m \ll M$ of A/D converters and RF modulation/demodulation chains is needed. In JSDM, users are grouped according to the similarity of their channel dominant subspaces, and these groups are separated by the analog beamforming stage, where the multiplexing gain in each group is achieved using the digital precoder. Therefore, it is apparent that extracting the channel subspace information of the $M$-dim channel vectors from snapshots of $m$-dim projections, with $m \ll M$, plays a fundamental role in JSDM implementation. #R##N#In this paper, we develop novel efficient algorithms that require sampling only $m = O(2\sqrt{M})$ specific array elements according to a coprime sampling scheme, and for a given $p \ll M$, return a $p$-dim beamformer that has a performance comparable with the best p-dim beamformer that can be designed from the full knowledge of the exact channel covariance matrix. We assess the performance of our proposed estimators both analytically and empirically via numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "millimeter wave beam selection using out of band spatial information"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-2 (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"
    - "low complexity massive mimo subspace estimation and tracking 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-3 (induced by source paper "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"):
  with members:
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "compressed channel feedback for correlated massive mimo systems"
    - "fundamental limits in correlated fading mimo broadcast channels benefits of transmit correlation diversity"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-4 (induced by source paper "low complexity massive mimo subspace estimation and tracking from low dimensional projections"):
  with members:
    - "a compact formulation for the ell_ 2 1 mixed norm minimization problem"
    - "massive mimo for next generation wireless systems"
    - "massive mimo meets hetnet interference coordination through spatial blanking"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"
    - "joint spatial division and multiplexing for mm wave channels"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-5 (induced by source paper "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "massive mimo for next generation wireless systems"
    - "joint spatial division and multiplexing for mm wave channels"
    - "atomic norm denoising with applications to line spectral estimation"
    - "subspace methods for joint sparse recovery"

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)