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 "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"):
  Abstract: This paper is concerned with the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement compared to the existing nonoverlapped designs and thus reduce the required channel estimation time. We develop a maximum likelihood estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon–Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using nonoverlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6 dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms.

Includes members:
  Member "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing channel estimation for ofdm with non gaussian multipath gains")
  Member "race a rate adaptive channel estimation approach for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ray of arrival passing for indirect beam training in cooperative millimeter wave mimo networks")
  Member "hierarchical codebook design for beamforming training in millimeter wave communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge cache assisted secure low latency millimeter wave transmission")
  Member "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training beam sequence design for millimeter wave mimo systems a pomdp framework")
  Member "iterative eigenvalue decomposition and multipath grouping tx rx joint beamformings for millimeter wave communications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient transceiver design for hybrid sub array architecture mimo systems")
  Member "iterative joint beamforming training with constant amplitude phased arrays in millimeter wave communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel estimation and transmission for intelligent reflecting surface assisted thz communications")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")

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)