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: "probabilistic theorem proving"
  Abstract: Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable elimination and belief propagation, neither of which take logical structure into account. We propose the first method that has the full power of both graphical model inference and first-order theorem proving (in finite domains with Herbrand interpretations). We first define probabilistic theorem proving, their generalization, as the problem of computing the probability of a logical formula given the probabilities or weights of a set of formulas. We then show how this can be reduced to the problem of lifted weighted model counting, and develop an efficient algorithm for the latter. We prove the correctness of this algorithm, investigate its properties, and show how it generalizes previous approaches. Experiments show that it greatly outperforms lifted variable elimination when logical structure is present. Finally, we propose an algorithm for approximate probabilistic theorem proving, and show that it can greatly outperform lifted belief propagation.

Connected through hyperedge HE-1 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "opennmt open source toolkit for neural machine translation"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-2 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "geodesic walks in polytopes"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-3 (induced by source paper "a critical look at the applicability of markov logic networks for music signal analysis"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"

Connected through hyperedge HE-4 (induced by source paper "on open universe causal reasoning"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "on the conditional logic of simulation models"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "learning the structure of dynamic probabilistic networks"
    - "church a language for generative models"
    - "nonparametric bayesian logic"
    - "towards common sense reasoning via conditional simulation legacies of turing in artificial intelligence"

Connected through hyperedge HE-5 (induced by source paper "generating and sampling orbits for lifted probabilistic inference"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "exploring unknown universes in probabilistic relational models"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "adaptive inference on general graphical models"

Connected through hyperedge HE-7 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-8 (induced by source paper "lifted filtering via exchangeable decomposition"):
  with members:
    - "sequential lifted bayesian filtering in multiset rewriting systems"
    - "exchangeable variable models"
    - "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"
    - "policy recognition in the abstract hidden markov model"

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)