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: "spook a system for probabilistic object oriented knowledge representation"
  Abstract: In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, {em Object-oriented Bayesian Netorks}, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many interesting aspects of complex domains: the existence of specific named objects, arbitrary relations between objects, and uncertainty over domain structure. These aspects are crucial in real-world domains such as battlefield awareness. In this paper, we present SPOOK, an implemented system that addresses these limitations. SPOOK implements a more expressive language that allows it to represent the battlespace domain naturally and compactly. We present a new inference algorithm that utilizes the model structure in a fundamental way, and show empirically that it achieves orders of magnitude speedup over existing approaches.

Connected through hyperedge HE-1 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "object oriented bayesian networks"
    - "algebraic model counting"

Connected through hyperedge HE-2 (induced by source paper "hypothesis management in situation specific network construction"):
  with members:
    - "constructing situation specific belief networks"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a probabilistic analysis of marker passing techniques for plan recognition"
    - "object oriented bayesian networks"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-3 (induced by source paper "causal mechanism based model construction"):
  with members:
    - "causality in bayesian belief networks"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "a generalization of the noisy or model"
    - "object oriented bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "toward general analysis of recursive probability models"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "context specific independence in bayesian networks"
    - "object oriented bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "lifted relational variational inference"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "nonparametric bayesian logic"
    - "lifted inference for relational continuous models"
    - "extended lifted inference with joint formulas"

Connected through hyperedge HE-6 (induced by source paper "template based inference in symmetric relational markov random fields"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "discriminative probabilistic models for relational data"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-7 (induced by source paper "lifted inference for relational continuous models"):
  with members:
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "object oriented bayesian networks"
    - "inference in hybrid bayesian networks using mixtures of gaussians"

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)