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: "low complexity near ml decoding of large non orthogonal stbcs using reactive tabu search"
  Abstract: Non-orthogonal space-time block codes (STBC) with {\em large dimensions} are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) {\em as well as} full transmit diversity. Decoding of non-orthogonal STBCs with large dimensions has been a challenge. In this paper, we present a reactive tabu search (RTS) based algorithm for decoding non-orthogonal STBCs from cyclic division algebras (CDA) having large dimensions. Under i.i.d fading and perfect channel state information at the receiver (CSIR), our simulation results show that RTS based decoding of $12\times 12$ STBC from CDA and 4-QAM with 288 real dimensions achieves $i)$ $10^{-3}$ uncoded BER at an SNR of just 0.5 dB away from SISO AWGN performance, and $ii)$ a coded BER performance close to within about 5 dB of the theoretical MIMO capacity, using rate-3/4 turbo code at a spectral efficiency of 18 bps/Hz. RTS is shown to achieve near SISO AWGN performance with less number of dimensions than with LAS algorithm (which we reported recently) at some extra complexity than LAS. We also report good BER performance of RTS when i.i.d fading and perfect CSIR assumptions are relaxed by considering a spatially correlated MIMO channel model, and by using a training based iterative RTS decoding/channel estimation scheme.

Connected through hyperedge HE-1 (induced by source paper "groupwise neighbor examination for tabu search detection in large mimo systems"):
  with members:
    - "near ml signal detection in large dimension linear vector channels using reactive tabu search"

Connected through hyperedge HE-2 (induced by source paper "two stage lasso admm signal detection algorithm for large scale mimo"):
  with members:
    - "five disruptive technology directions for 5g"
    - "sparse subspace clustering algorithm theory and applications"
    - "near ml signal detection in large dimension linear vector channels using reactive tabu search"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a hybrid rts bp algorithm for improved detection of large mimo m qam signals"):
  with members:
    - "near optimal detection in mimo systems using gibbs sampling"
    - "near ml signal detection in large dimension linear vector channels using reactive tabu search"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"

Connected through hyperedge HE-5 (induced by source paper "asymptotic analysis of the performance of las algorithm for large mimo detection"):
  with members:
    - "belief propagation based decoding of large non orthogonal stbcs"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"
    - "large mimo detection a low complexity detector at high spectral efficiencies"
    - "reduced complexity sphere decoding for square qam via a new lattice representation"
    - "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)