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: "tractability through exchangeability a new perspective on efficient probabilistic inference"
  Abstract: Exchangeability is a central notion in statistics and probability theory. The assumption that an infinite sequence of data points is exchangeable is at the core of Bayesian statistics. However, finite exchangeability as a statistical property that renders probabilistic inference tractable is less well-understood. We develop a theory of finite exchangeability and its relation to tractable probabilistic inference. The theory is complementary to that of independence and conditional independence. We show that tractable inference in probabilistic models with high treewidth and millions of variables can be understood using the notion of finite (partial) exchangeability. We also show that existing lifted inference algorithms implicitly utilize a combination of conditional independence and partial exchangeability.

Connected through hyperedge HE-1 (induced by source paper "relational pooling for graph representations"):
  with members:
    - "on the universality of invariant networks"
    - "pitfalls of graph neural network evaluation"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "generating and sampling orbits for lifted probabilistic inference"):
  with members:
    - "lifted probabilistic inference for asymmetric graphical models"
    - "symmetry aware marginal density estimation"
    - "practical graph isomorphism ii"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "exploring unknown universes in probabilistic relational models"):
  with members:
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "adaptive inference on general graphical models"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "lower complexity bounds for lifted inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"
    - "symmetric weighted first order model counting"

Connected through hyperedge HE-5 (induced by source paper "lifted probabilistic inference for asymmetric graphical models"):
  with members:
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "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"
    - "counting belief propagation"

Connected through hyperedge HE-6 (induced by source paper "exchangeable variable models"):
  with members:
    - "the bayesian structural em algorithm"
    - "fast exact inference for recursive cardinality models"
    - "markov chains on orbits of permutation groups"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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.AI(Artificial Intelligence)