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: "projecting ising model parameters for fast mixing"
  Abstract: Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.

Connected through hyperedge HE-1 (induced by source paper "correlated equilibria for approximate variational inference in mrfs"):
  with members:
    - "graphical potential games"
    - "on sparse discretization for graphical games"
    - "graphical models for game theory"
    - "adaptive importance sampling for estimation in structured domains"
    - "a new class of upper bounds on the log partition function"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "settling the complexity of computing two player nash equilibria"
    - "computing nash equilibria approximation and smoothed complexity"

Connected through hyperedge HE-2 (induced by source paper "maximum likelihood learning with arbitrary treewidth via fast mixing parameter sets"):
  with members:
    - "learning fast mixing models for structured prediction"
    - "projecting markov random field parameters for fast mixing"
    - "asymptotic efficiency of deterministic estimators for discrete energy based models ratio matching and pseudolikelihood"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "projecting markov random field parameters for fast mixing"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"

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.LG(Machine Learning)