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 "channel coherence classification with frame shifting in massive mimo systems"):
  Abstract: This paper considers the uplink pilot overhead in a time division duplexing (TDD) massive Multiple Input Multiple Output (MIMO) mobile systems. A common scenario of conventional massive MIMO systems is a Base Station (BS) serving all user terminals (UTs) in the cell with the same TDD frame format that fits the coherence interval of the worst-case scenario of user mobility (e.g. a moving train with velocity 300 Km/s). Furthermore, the BS has to estimate all the channels each time-slot for all users even for those with long coherence intervals. In fact, within the same cell, sensors or pedestrian with low mobility UTs (e.g. moving 1.38 m/s) share the same short TDD frame and thus are obliged to upload their pilots each time-slot. The channel coherence interval of the pedestrian UTs with a carrier frequency of 1.9 GHz can be as long as 60 times that of the train passenger users. In other words, conventional techniques waste 59-uploaded pilot sequences for channel estimation. In this paper, we are aware of the resources waste due to various coherence intervals among different user mobility. We classify users based on their coherence interval length, and we propose to skip uploading pilots of UTs with large coherence intervals. Then, we shift frames with the same pilot reused sequence toward an empty pilot time-slot. Simulation results had proved that the proposed technique overcome the performance of conventional massive MIMO systems in both energy and spectral efficiency.

Includes members:
  Member "selective uplink training for massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally orthogonal training design for cloud rans based on graph coloring")
  Member "massive mimo 10 myths and one grand question":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spatial basis coverage approach for uplink training and scheduling in massive mimo systems")
  Member "pilot clustering in asymmetric massive mimo networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive pilot clustering in heterogeneous massive mimo networks")
  Member "smart pilot assignment for massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")
  Member "blind pilot decontamination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)