human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"):
  Abstract: Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold (for finding a vector in the codebook which maximizes beamforming gain) and noncoherent sequence detection (for maximum likelihood decoding subject to uncertainty in the channel gain). Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Monte Carlo simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.

Includes members:
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "massive mimo how many antennas do we need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "energy and spectral efficiency of very large multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "capacity of a multiple antenna fading channel with a quantized precoding matrix":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback")
  Member "glrt optimal noncoherent lattice decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient optimal joint channel estimation and data detection for massive mimo systems")
  Member "mimo broadcast channels with finite rate feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "limited feedback beamforming over temporally correlated channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning")

Question: Please tell me which class the source paper 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)