human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "coherent detection of turbo coded ofdm signals transmitted through frequency selective rayleigh fading channels with receiver diversity and increased throughput"
  Abstract: In this work, we discuss techniques for coherently detecting turbo coded orthogonal frequency division multiplexed (OFDM) signals, transmitted through frequency selective Rayleigh (the magnitude of each channel tap is Rayleigh distributed) fading channels having a uniform power delay profile. The channel output is further distorted by a carrier frequency and phase offset, besides additive white Gaussian noise. A new frame structure for OFDM, consisting of a known preamble, cyclic prefix, data and known postamble is proposed, which has a higher throughput compared to the earlier work. A robust turbo decoder is proposed, which functions effectively over a wide range of signal-to-noise ratio (SNR). Simulation results show that it is possible to achieve a bit-error-rate (BER) of $$10^{-5}$$10-5 at an SNR per bit as low as 8 dB and throughput of 82.84 %, using a single transmit and two receive antennas. We also demonstrate that the practical coherent receiver requires just about 1 dB more power compared to that of an ideal coherent receiver, to attain a BER of $$10^{-5}$$10-5. The key contribution to the good performance of the practical coherent receiver is due to the use of a long preamble (512 QPSK symbols), which is perhaps not specified in any of the current wireless communication standards. We have also shown from computer simulations that, it is possible to obtain even better BER performance, using a better code. A simple and approximate Cramer---Rao bound on the variance of the frequency offset estimation error for coherent detection, is derived. The proposed algorithms are well suited for implementation on a DSP-platform.

Connected through hyperedge HE-1 (induced by source paper "on the probability of erasure for mimo ofdm"):
  with members:
    - "data detection in single user massive mimo using re transmissions"
    - "near capacity signaling over fading channels using coherent turbo coded ofdm and massive mimo"

Connected through hyperedge HE-2 (induced by source paper "data detection in single user massive mimo using re transmissions"):
  with members:
    - "near capacity signaling over fading channels using coherent turbo coded ofdm and massive mimo"
    - "doubly massive mmwave mimo systems using very large antenna arrays at both transmitter and receiver"
    - "a multiband ofdma heterogeneous network for millimeter wave 5g wireless applications"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "near capacity signaling over fading channels using coherent turbo coded ofdm and massive mimo"):
  with members:
    - "a multiband ofdma heterogeneous network for millimeter wave 5g wireless applications"
    - "index modulation techniques for 5g wireless networks"
    - "what will 5g be"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "scaling up mimo opportunities and challenges with very large arrays"

Question: Please tell me which class the center node 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)