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: "relative entropy probabilistic inference and ai"
  Abstract: Various properties of relative entropy have led to its widespread use in information theory. These properties suggest that relative entropy has a role to play in systems that attempt to perform inference in terms of probability distributions. In this paper, I will review some basic properties of relative entropy as well as its role in probabilistic inference. I will also mention briefly a few existing and potential applications of relative entropy to so-called artificial intelligence (AI).

Connected through hyperedge HE-1 (induced by source paper "learning from both experts and data"):
  with members:
    - "a model based reinforcement learning approach for a rare disease diagnostic task"
    - "bayesian reinforcement learning a survey"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "uncertain reasoning using maximum entropy inference"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"
    - "a polynomial time algorithm for finding bayesian probabilities from marginal constraints"

Connected through hyperedge HE-2 (induced by source paper "generating new beliefs from old"):
  with members:
    - "a logic for default reasoning about probabilities"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-3 (induced by source paper "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"):
  with members:
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-4 (induced by source paper "a logic for default reasoning about probabilities"):
  with members:
    - "random worlds and maximum entropy"

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)