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: "approximate knowledge compilation by online collapsed importance sampling"
  Abstract: We introduce collapsed compilation, a novel approximate inference algorithm for discrete probabilistic graphical models. It is a collapsed sampling algorithm that incrementally selects which variable to sample next based on the partial sample obtained so far. This online collapsing, together with knowledge compilation inference on the remaining variables, naturally exploits local structure and context- specific independence in the distribution. These properties are naturally exploited in exact inference, but are difficult to harness for approximate inference. More- over, by having a partially compiled circuit available during sampling, collapsed compilation has access to a highly effective proposal distribution for importance sampling. Our experimental evaluation shows that collapsed compilation performs well on standard benchmarks. In particular, when the amount of exact inference is equally limited, collapsed compilation is competitive with the state of the art, and outperforms it on several benchmarks.

Connected through hyperedge HE-1 (induced by source paper "towards efficient discrete integration via adaptive quantile queries"):
  with members:
    - "probabilistic model counting with short xors"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-2 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "how powerful are graph neural networks"
    - "inference in probabilistic graphical models by graph neural networks"
    - "on hashing based approaches to approximate dnf counting"
    - "gated graph sequence neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-3 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-4 (induced by source paper "adawish faster discrete integration via adaptive quantiles"):
  with members:
    - "probabilistic model counting with short xors"
    - "closing the gap between short and long xors for model counting"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-5 (induced by source paper "symbolic exact inference for discrete probabilistic programs"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "measure transformer semantics for bayesian machine learning"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "a knowledge compilation map"

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)