human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"):
  Abstract: Sampling from the lattice Gaussian distribution is emerging as an important problem in coding and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm from Markov chain Monte Carlo (MCMC) methods is adapted for lattice Gaussian sampling. Two MH-based algorithms are proposed, which overcome the restriction suffered by the default Klein's algorithm. The first one, referred to as the independent Metropolis-Hastings-Klein (MHK) algorithm, tries to establish a Markov chain through an independent proposal distribution. We show that the Markov chain arising from the independent MHK algorithm is uniformly ergodic, namely, it converges to the stationary distribution exponentially fast regardless of the initial state. Moreover, the rate of convergence is explicitly calculated in terms of the theta series, leading to a predictable mixing time. In order to further exploit the convergence potential, a symmetric Metropolis-Klein (SMK) algorithm is proposed. It is proven that the Markov chain induced by the SMK algorithm is geometrically ergodic, where a reasonable selection of the initial state is capable to enhance the convergence performance.

Includes members:
  Member "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity":
    also connected through other hyperedges:
      (none)
  Member "discrete gaussian sampling reduces to cvp and svp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "markov chain monte carlo algorithms for lattice gaussian sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "semantically secure lattice codes for the gaussian wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantically secure lattice codes for compound mimo channels")
  Member "achieving awgn channel capacity with lattice gaussian coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "well rounded lattices towards optimal coset codes for gaussian and fading wiretap channels")
  Member "decoding by sampling part ii derandomization and soft output decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "on the proximity factors of lattice reduction aided decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")

Question: Please tell me which class the source paper 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)