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 "fountain code inspired channel estimation for multi user millimeter wave mimo systems"):
  Abstract: This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/receive beamforming directions until the channel estimate converges. We formulate the channel estimation process as a compressed sensing problem and apply a sparse estimation approach to recover the virtual channel information. As SWIFT does not adapt the BS's transmitting beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform existing random-beamforming based approaches that use a fixed number of measurements, over a range of signal-to-noise ratios and channel coherence times.

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 "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure communication for spatially sparse millimeter wave massive mimo channels via hybrid precoding")
  Member "race a rate adaptive channel estimation approach for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ray of arrival passing for indirect beam training in cooperative millimeter wave mimo networks")
  Member "an overview of signal processing techniques for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "massive mimo ten myths and one critical question":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "compressed sensing based multi user millimeter wave systems how many measurements are needed":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  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 "near capacity adaptive analog fountain codes for wireless channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two layer coded channel access with collision resolution design and analysis")

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)