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 "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"):
  Abstract: This paper considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural network models are leveraged to learn the non-trivial mapping from quantized received measurements to channels. For that, we derive the sufficient length and structure of the pilot sequence to guarantee the existence of this mapping function. This leads to the interesting, and \textit{counter-intuitive}, observation that when more antennas are employed by the massive MIMO base station, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Equivalently, for the same channel estimation performance, this means that when more antennas are employed, fewer pilots are required. This observation is also analytically proved for some special channel models. Simulation results confirm our observations and show that more antennas lead to better channel estimation both in terms of the normalized mean squared error and the achievable signal-to-noise ratio per antenna.

Includes members:
  Member "deep learning based channel estimation for massive mimo with mixed resolution adcs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "deep learning for tdd and fdd massive mimo mapping channels in space and frequency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "one bit ofdm receivers via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "deep learning coordinated beamforming for highly mobile millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "millimeter wave vehicular communication to support massive automotive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "channel estimation in broadband millimeter wave mimo systems with few bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")

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)