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: "exchangeable variable models"
  Abstract: A sequence of random variables is exchangeable if its joint distribution is invariant under variable permutations. We introduce exchangeable variable models (EVMs) as a novel class of probabilistic models whose basic building blocks are partially exchangeable sequences, a generalization of exchangeable sequences. We prove that a family of tractable EVMs is optimal under zero-one loss for a large class of functions, including parity and threshold functions, and strictly subsumes existing tractable independence-based model families. Extensive experiments show that EVMs outperform state of the art classifiers such as SVMs and probabilistic models which are solely based on independence assumptions.

Connected through hyperedge HE-1 (induced by source paper "lifted filtering via exchangeable decomposition"):
  with members:
    - "sequential lifted bayesian filtering in multiset rewriting systems"
    - "tractable inference for complex stochastic processes"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "sum product networks a new deep architecture"
    - "sampling first order logical particles"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "policy recognition in the abstract hidden markov model"

Connected through hyperedge HE-2 (induced by source paper "searching for topological symmetry in data haystack"):
  with members:
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "reified context models"):
  with members:
    - "learning fast mixing models for structured prediction"
    - "mean field networks"
    - "sum product networks a new deep architecture"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "tightening lp relaxations for map using message passing"
    - "a new class of upper bounds on the log partition function"
    - "learning dependency based compositional semantics"
    - "search based structured prediction"

Connected through hyperedge HE-4 (induced by source paper "tractability through exchangeability a new perspective on efficient probabilistic inference"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "context specific independence in bayesian networks"
    - "symmetry aware marginal density estimation"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

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)