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 optimal detection in mimo systems using gibbs sampling"
  Abstract: In this paper we study a Markov Chain Monte Carlo (MCMC) Gibbs sampler for solving the integer least-squares problem. In digital communication the problem is equivalent to performing Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output (MIMO) systems. While the use of MCMC methods for such problems has already been proposed, our method is novel in that we optimize the "temperature" parameter so that in steady state, i.e. after the Markov chain has mixed, there is only polynomially (rather than exponentially) small probability of encountering the optimal solution. More precisely, we obtain the largest value of the temperature parameter for this to occur, since the higher the temperature, the faster the mixing. This is in contrast to simulated annealing techniques where, rather than being held fixed, the temperature parameter is tended to zero. Simulations suggest that the resulting Gibbs sampler provides a computationally efficient way of achieving approximative ML detection in MIMO systems having a huge number of transmit and receive dimensions. In fact, they further suggest that the Markov chain is rapidly mixing. Thus, it has been observed that even in cases were ML detection using, e.g. sphere decoding becomes infeasible, the Gibbs sampler can still offer a near-optimal solution using much less computations.

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 "probabilistic mimo symbol detection with expectation consistency approximate inference"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "spatially coupled ldpc codes constructed from protographs"
    - "optimality of large mimo detection via approximate message passing"
    - "fifty years of mimo detection the road to large scale mimos"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"

Connected through hyperedge HE-3 (induced by source paper "on otfs modulation for high doppler fading channels"):
  with members:
    - "a simple two stage equalizer with simplified orthogonal time frequency space modulation over rapidly time varying channels"
    - "analysis of discrete time mimo ofdm based orthogonal time frequency space modulation"
    - "low complexity modem structure for ofdm based orthogonal time frequency space modulation"
    - "otfs orthogonal time frequency space"

Connected through hyperedge HE-4 (induced by source paper "achieving near map performance with an excited markov chain monte carlo mimo detector"):
  with members:
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"

Connected through hyperedge HE-5 (induced by source paper "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "on the mixing time of markov chain monte carlo for integer least square problems"

Connected through hyperedge HE-6 (induced by source paper "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "efficient soft input soft output tree detection via an improved path metric"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "faster recursions in sphere decoding"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"

Connected through hyperedge HE-7 (induced by source paper "a novel mcmc based receiver for large scale uplink multiuser mimo systems"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "massive mimo how many antennas do we need"
    - "low complexity detection in large dimension mimo isi channels using graphical models"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-8 (induced by source paper "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"):
  with members:
    - "efficient soft input soft output tree detection via an improved path metric"
    - "lattice sequential decoder for coded mimo channel performance and complexity analysis"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "faster recursions in sphere decoding"
    - "lattice sequential decoding for last coded mimo channels achievable rate dmt and complexity analysis"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "soft input soft output single tree search sphere decoding"

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)