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: "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
  Abstract: This paper addresses the problem of uplink and downlink channel estimation in FDD Massive MIMO systems. By utilizing sparse recovery and compressive sensing algorithms, we are able to improve the accuracy of the uplink/downlink channel estimation and reduce the number of uplink/downlink pilot symbols. Such successful channel estimation builds upon the assumption that the channel can be sparsely represented under some basis/dictionary. Previous works model the channel using some predefined basis/dictionary, while in this work, we present a dictionary learning based channel model such that a dictionary is learned from comprehensively collected channel measurements. The learned dictionary adapts specifically to the cell characteristics and promotes a more efficient and robust channel representation, which in turn improves the performance of the channel estimation. Furthermore, we extend the dictionary learning based channel model into a joint uplink/downlink learning framework by observing the reciprocity of the AOA/AOD between the uplink/downlink transmission, and propose a joint channel estimation algorithm that combines the uplink and downlink received training signals to obtain a more accurate channel estimate. In other words, the downlink training overhead, which is a bottleneck in FDD Massive MIMO system, can be reduced by utilizing the information from simpler uplink training.

Connected through hyperedge HE-1 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection"):
  with members:
    - "semi blind channel and signal estimation for uplink massive mimo with channel sparsity"
    - "multi band covariance interpolation with applications in massive mimo"
    - "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "massive mimo for next generation wireless systems"
    - "statistical beamforming on the grassmann manifold for the two user broadcast channel"

Connected through hyperedge HE-2 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "channel estimation in massive mimo systems"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-3 (induced by source paper "dictionary learning for channel estimation in hybrid frequency selective mmwave mimo systems"):
  with members:
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "separable dictionary learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"

Connected through hyperedge HE-5 (induced by source paper "low overhead weighted graph coloring based two layer precoding for fdd massive mimo systems"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-6 (induced by source paper "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"):
  with members:
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "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"
    - "trellis extended codebooks and successive phase adjustment a path from lte advanced to fdd massive mimo systems"

Connected through hyperedge HE-7 (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"
    - "compressed channel feedback for correlated massive mimo systems"
    - "channel vector subspace estimation from low dimensional projections"
    - "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"

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)