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: "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
  Abstract: We consider a massive MIMO system based on Time Division Duplexing (TDD) and channel reciprocity, where the base stations (BSs) learn the channel vectors of their users via the pilots transmitted by the users in the uplink (UL). It is well-known that, in the limit of very large number of BS antennas, the system performance is limited by pilot contamination, due to the fact that the same set of orthogonal pilots is reused in multiple cells. In this paper, we propose a low-complexity algorithm that uses the received UL wideband pilot snapshots in an observation window comprising several coherence blocks (CBs) to obtain an estimate of the angle-delay Power Spread Function (PSF) of the received signal. This is generally given by the sum of the angle-delay PSF of the desired user and the angle-delay PSFs of the copilot users (CPUs), i.e., the users re-using the same pilot dimensions in other cells/sectors. We propose supervised and unsupervised clustering algorithms to decompose the estimated PSF and isolate the part corresponding to the desired user only. We use this decomposition to obtain an estimate of the covariance matrix of the user wideband channel vector, which we exploit to decontaminate the desired user channel estimate by applying Minimum Mean Squared Error (MMSE) smoothing filter, i.e., the optimal channel interpolator in the MMSE sense. We also propose an effective low-complexity approximation/implementation of this smoothing filter. We use numerical simulations to assess the performance of our proposed method, and compare it with other recently proposed schemes that use the same idea of separability of users in the angle-delay domain.

Connected through hyperedge HE-1 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "faster activity and data detection in massive random access a multi armed bandit approach"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "grant free rateless multiple access a novel massive access scheme for internet of things"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-2 (induced by source paper "scalable cell free massive mimo systems"):
  with members:
    - "a new look at cell free massive mimo making it practical with dynamic cooperation"
    - "scalability aspects of cell free massive mimo"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "covariance matrix estimation for massive mimo"
    - "massive mimo in sub 6 ghz and mmwave physical practical and use case differences"
    - "on the performance of backhaul constrained cell free massive mimo with linear receivers"
    - "fog massive mimo a user centric seamless hot spot architecture"
    - "covariance matrix estimation in massive mimo"

Connected through hyperedge HE-3 (induced by source paper "data aided secure massive mimo transmission under the pilot contamination attack"):
  with members:
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"
    - "pilot spoofing attack by multiple eavesdroppers"
    - "multiple antennas secure transmission under pilot spoofing and jamming attack"
    - "secure transmission with large numbers of antennas and finite alphabet inputs"
    - "analysis and design of secure massive mimo systems in the presence of hardware impairments"
    - "performance limits of massive mimo systems based on bayes optimal inference"
    - "secure massive mimo transmission with an active eavesdropper"
    - "large scale mimo relaying techniques for physical layer security af or df"

Connected through hyperedge HE-4 (induced by source paper "on the spectral efficiency for massive mimo systems with imperfect spacial covariance information"):
  with members:
    - "covariance matrix estimation for massive mimo"
    - "covariance matrix estimation in massive mimo"
    - "massive mimo has unlimited capacity"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "massive mimo with imperfect channel covariance information"
    - "path loss shadow fading and line of sight probability models for 5g urban macro cellular scenarios"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-5 (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"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"

Connected through hyperedge HE-6 (induced by source paper "massive mimo with spatially correlated rician fading channels"):
  with members:
    - "covariance matrix estimation for massive mimo"
    - "uplink spectral efficiency of massive mimo with spatially correlated rician fading"
    - "covariance matrix estimation in massive mimo"
    - "impact of line of sight and unequal spatial correlation on uplink mu mimo systems"
    - "massive mimo with imperfect channel covariance information"
    - "asymptotic analysis of multicell massive mimo over rician fading channels"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-7 (induced by source paper "capacity scaling of massive mimo in strong spatial correlation regimes"):
  with members:
    - "massive mimo has unlimited capacity"
    - "on the miso channel with feedback can infinitely massive antennas achieve infinite capacity"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "on the role of transmit correlation diversity in multiuser mimo systems"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "hybrid beamforming for massive mimo a survey"
    - "on the high snr capacity of the gaussian interference channel and new capacity bounds"
    - "massive mimo performance evaluation based on measured propagation data"

Connected through hyperedge HE-8 (induced by source paper "massive mimo has unlimited capacity"):
  with members:
    - "massive mimo with imperfect channel covariance information"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "massive mimo with multi cell mmse processing exploiting all pilots for interference suppression"
    - "massive mimo in real propagation environments do all antennas contribute equally"
    - "massive mimo performance evaluation based on measured propagation data"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"

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)