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: "near ml signal detection in large dimension linear vector channels using reactive tabu search"
  Abstract: Low-complexity near-optimal signal detection in large dimensional communication systems is a challenge. In this paper, we present a reactive tabu search (RTS) algorithm, a heuristic based combinatorial optimization technique, to achieve low-complexity near-maximum likelihood (ML) signal detection in linear vector channels with large dimensions. Two practically important large-dimension linear vector channels are considered: i) multiple-input multiple-output (MIMO) channels with large number (tens) of transmit and receive antennas, and ii) severely delay-spread MIMO inter-symbol interference (ISI) channels with large number (tens to hundreds) of multipath components. These channels are of interest because the former offers the benefit of increased spectral efficiency (several tens of bps /Hz) and the latter offers the benefit of high time-diversity orde rs. Our simulation results show that, while algorithms including variants of sphere decoding do not scale well for large dimensions, the proposed RTS algorithm scales well for signal detection in large dimensions while achieving increasingly closer to ML performance for increasing number of dimensions. Index Terms—Linear vector channels, large dimensions, low- complexity detection, near-ML performance, V-BLAST, non- orthogonal STBCs, MIMO-ISI channels, UWB, severe delay spread, tabu search.

Connected through hyperedge HE-1 (induced by source paper "deep learning aided tabu search detection for large mimo systems"):
  with members:
    - "learning to detect"
    - "detection algorithms for communication systems using deep learning"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-2 (induced by source paper "groupwise neighbor examination for tabu search detection in large mimo systems"):
  with members:
    - "low complexity near ml decoding of large non orthogonal stbcs using reactive tabu search"

Connected through hyperedge HE-3 (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"
    - "low complexity near ml decoding of large non orthogonal stbcs using reactive tabu search"

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"
    - "low complexity near ml decoding of large non orthogonal stbcs using reactive tabu search"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"

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)