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: "markov chain monte carlo algorithms for lattice gaussian sampling"
  Abstract: Sampling from a lattice Gaussian distribution is emerging as an important problem in various areas such as coding and cryptography. The default sampling algorithm --- Klein's algorithm yields a distribution close to the lattice Gaussian only if the standard deviation is sufficiently large. In this paper, we propose the Markov chain Monte Carlo (MCMC) method for lattice Gaussian sampling when this condition is not satisfied. In particular, we present a sampling algorithm based on Gibbs sampling, which converges to the target lattice Gaussian distribution for any value of the standard deviation. To improve the convergence rate, a more efficient algorithm referred to as Gibbs-Klein sampling is proposed, which samples block by block using Klein's algorithm. We show that Gibbs-Klein sampling yields a distribution close to the target lattice Gaussian, under a less stringent condition than that of the original Klein algorithm.

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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "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 "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-3 (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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "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-4 (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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "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-5 (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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "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-6 (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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "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)