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: "monte carlo algorithms for the partition function and information rates of two dimensional channels"
  Abstract: The paper proposes Monte Carlo algorithms for the computation of the information rate of two-dimensional source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noiseless capacity, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo estimate of a partition function. The proposed algorithms use tree-based Gibbs sampling and multilayer (multitemperature) importance sampling. The viability of the proposed algorithms is demonstrated by simulation results.

Connected through hyperedge HE-1 (induced by source paper "factor graphs for quantum probabilities"):
  with members:
    - "double edge factor graphs definition properties and examples"
    - "on sparsity by nuv em gaussian message passing and kalman smoothing"
    - "holographic transformation belief propagation and loop calculus for generalized probabilistic theories"
    - "the bethe permanent of a nonnegative matrix"
    - "a factor graph representation of probabilities in quantum mechanics"
    - "normal factor graphs a diagrammatic approach to linear algebra"
    - "partition functions of normal factor graphs"
    - "normal factor graphs and holographic transformations"

Connected through hyperedge HE-2 (induced by source paper "on the entropy computation of large complex gaussian mixture distributions"):
  with members:
    - "large system analysis of correlated mimo multiple access channels with arbitrary signaling in the presence of interference"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "capacity estimation of two dimensional channels using sequential monte carlo"):
  with members:
    - "generalized belief propagation for the noiseless capacity and information rates of run length limited constraints"

Connected through hyperedge HE-4 (induced by source paper "partition function of the ising model via factor graph duality"):
  with members:
    - "partition functions of normal factor graphs"
    - "normal factor graphs and holographic transformations"

Connected through hyperedge HE-5 (induced by source paper "generalized belief propagation for the noiseless capacity and information rates of run length limited constraints"):
  with members:
    - "on the choice of regions for generalized belief propagation"

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)