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: "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
  Abstract: We introduce an optimized Markov chain Monte Carlo (MCMC) technique for solving integer least-squares (ILS) problems, which include maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) systems. Two factors contribute to its speed of finding the optimal solution: the probability of encountering the optimal solution when the Markov chain has converged to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal “temperature” parameter value, so that once the Markov chain has mixed to its stationary distribution, there is a polynomially small probability ( 1/poly(N), instead of exponentially small) of encountering the optimal solution, where N is the system dimension. This temperature is shown to be O(√{SNR}/ln(N)), where SNR > 2ln(N) is the SNR. Second, we study the mixing time of the underlying Markov chain of the MCMC detector. We find that, the mixing time is closely related to whether there is a local minimum in the ILS problem's lattice structure. For some lattices without local minima, the mixing time is independent of SNR, and grows polynomially in N. Conventional wisdom proposed to set temperature as the noise standard deviation, but our results show that, under such a temperature, the mixing time grows unbounded with SNR if the lattice has local minima. Our results suggest that, very often the temperature should instead be scaling at least as Ω(√{SNR}). Simulation results show that the optimized MCMC detector efficiently achieves approximately ML detection in MIMO systems having a huge number of transmit and receive dimensions.

Connected through hyperedge HE-1 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "massive mimo for next generation wireless systems"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "decoding by sampling part ii derandomization and soft output decoding"

Connected through hyperedge HE-2 (induced by source paper "an intrinsically motivated approach for learning highly exploring and fast mixing policies"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-3 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "boosted kz and lll algorithms"
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "polar codes and polar lattices for independent fading channels"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "fifty years of mimo detection the road to large scale mimos"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"

Connected through hyperedge HE-4 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization"):
  with members:
    - "boosted kz and lll algorithms"
    - "polar codes and polar lattices for independent fading channels"
    - "scan order in gibbs sampling models in which it matters and bounds on how much"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "decoding by sampling part ii derandomization and soft output decoding"

Connected through hyperedge HE-5 (induced by source paper "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"):
  with members:
    - "independent metropolis hastings klein algorithm for lattice gaussian sampling"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "massive mimo for next generation wireless systems"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "on the closest vector problem with a distance guarantee"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "achieving near map performance with an excited markov chain monte carlo mimo detector"):
  with members:
    - "near optimal detection in mimo systems using gibbs sampling"

Connected through hyperedge HE-7 (induced by source paper "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "decoding by sampling part ii derandomization and soft output decoding"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"

Connected through hyperedge HE-8 (induced by source paper "independent metropolis hastings klein algorithm for lattice gaussian sampling"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "decoding by sampling part ii derandomization and soft output decoding"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance 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)