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: "representation dependence in probabilistic inference"
  Abstract: Non-deductive reasoning systems are often {\em representation dependent}: representing the same situation in two different ways may cause such a system to return two different answers. Some have viewed this as a significant problem. For example, the principle of maximum entropy has been subjected to much criticism due to its representation dependence. There has, however, been almost no work investigating representation dependence. In this paper, we formalize this notion and show that it is not a problem specific to maximum entropy. In fact, we show that any representation-independent probabilistic inference procedure that ignores irrelevant information is essentially entailment, in a precise sense. Moreover, we show that representation independence is incompatible with even a weak default assumption of independence. We then show that invariance under a restricted class of representation changes can form a reasonable compromise between representation independence and other desiderata, and provide a construction of a family of inference procedures that provides such restricted representation independence, using relative entropy.

Connected through hyperedge HE-1 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "context specific independence in bayesian networks"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"

Connected through hyperedge HE-2 (induced by source paper "abstracting probabilistic relational models"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "bisimulation based approximate lifted inference"
    - "solving sat and bayesian inference with backtracking search"
    - "an abstract monte carlo method for the analysis of probabilistic programs"

Connected through hyperedge HE-3 (induced by source paper "probabilistic reasoning in the description logic alcp with the principle of maximum entropy full version"):
  with members:
    - "probabilistic deduction with conditional constraints over basic events"

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)