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: "a hybrid rts bp algorithm for improved detection of large mimo m qam signals"
  Abstract: Low-complexity near-optimal detection of large-MIMO signals has attracted recent research. Recently, we proposed a local neighborhood search algorithm, namely `reactive tabu search' (RTS) algorithm, as well as a factor-graph based `belief propagation' (BP) algorithm for low-complexity large-MIMO detection. The motivation for the present work arises from the following two observations on the above two algorithms: $i)$ RTS works for general M-QAM. Although RTS was shown to achieve close to optimal performance for 4-QAM in large dimensions, significant performance improvement was still possible for higher-order QAM (e.g., 16- and 64-QAM). ii) BP also was shown to achieve near-optimal performance for large dimensions, but only for $\{\pm 1\}$ alphabet. In this paper, we improve the large-MIMO detection performance of higher-order QAM signals by using a hybrid algorithm that employs RTS and BP. In particular, motivated by the observation that when a detection error occurs at the RTS output, the least significant bits (LSB) of the symbols are mostly in error, we propose to first reconstruct and cancel the interference due to bits other than LSBs at the RTS output and feed the interference cancelled received signal to the BP algorithm to improve the reliability of the LSBs. The output of the BP is then fed back to RTS for the next iteration. Our simulation results show that in a 32 x 32 V-BLAST system, the proposed RTS-BP algorithm performs better than RTS by about 3.5 dB at $10^{-3}$ uncoded BER and by about 2.5 dB at $3\times 10^{-4}$ rate-3/4 turbo coded BER with 64-QAM at the same order of complexity as RTS. We also illustrate the performance of large-MIMO detection in frequency-selective fading channels.

Connected through hyperedge HE-1 (induced by source paper "fifty years of mimo detection the road to large scale mimos"):
  with members:
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "an esprit based approach for 2 d localization of incoherently distributed sources in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "approximate bayesian probabilistic data association aided iterative detection for mimo systems using arbitrary m ary modulation"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-2 (induced by source paper "low complexity detection in large dimension mimo isi channels using graphical models"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "belief propagation based decoding of large non orthogonal stbcs"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"
    - "sufficient conditions for convergence of the sum product algorithm"
    - "improved message passing for inference in densely connected systems"
    - "belief propagation based multi user detection"

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)