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 "near capacity signaling over fading channels using coherent turbo coded ofdm and massive mimo"):
  Abstract: The minimum average signal-to-noise ratio (SNR) per bit required for error-free transmission over a fading channel is derived, and is shown to be equal to that of the additive white Gaussian noise (AWGN) channel, which is $-1.6$ dB. Discrete-time algorithms are presented for timing and carrier synchronization, as well as channel estimation, for turbo coded multiple input multiple output (MIMO) orthogonal frequency division multiplexed (OFDM) systems. Simulation results show that it is possible to achieve a bit error rate of $10^{-5}$ at an average SNR per bit of 5.5 dB, using two transmit and two receive antennas. We then propose a near-capacity signaling method in which each transmit antenna uses a different carrier frequency. Using the near-capacity approach, we show that it is possible to achieve a BER of $2\times 10^{-5}$ at an average SNR per bit of just 2.5 dB, with one receive antenna for each transmit antenna. When the number of receive antennas for each transmit antenna is increased to 128, then a BER of $2\times 10^{-5}$ is attained at an average SNR per bit of 1.25 dB. In all cases, the number of transmit antennas is two and the spectral efficiency is 1 bit/transmission or 1 bit/sec/Hz. In other words, each transmit antenna sends 0.5 bit/transmission. It is possible to obtain higher spectral efficiency by increasing the number of transmit antennas, with no loss in BER performance, as long as each transmit antenna uses a different carrier frequency. The transmitted signal spectrum for the near-capacity approach can be restricted by pulse-shaping. In all the simulations, a four-state turbo code is used. The corresponding turbo decoder uses eight iterations. The algorithms can be implemented on programmable hardware and there is a large scope for parallel processing.

Includes members:
  Member "a multiband ofdma heterogeneous network for millimeter wave 5g wireless applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data detection in single user massive mimo using re transmissions")
  Member "index modulation techniques for 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on spatial modulation in emerging wireless systems research progresses and applications")
  Member "coherent detection of turbo coded ofdm signals transmitted through frequency selective rayleigh fading channels with receiver diversity and increased throughput":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the probability of erasure for mimo ofdm")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "massive mimo for maximal spectral efficiency how many users and pilots should be allocated":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massive mimo for internet of things iot connectivity")
  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")

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)