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: "inter causal independence and heterogeneous factorization"
  Abstract: It is well known that conditional independence can be used to factorize a joint probability into a multiplication of conditional probabilities. This paper proposes a constructive definition of inter-causal independence, which can be used to further factorize a conditional probability. An inference algorithm is developed, which makes use of both conditional independence and inter-causal independence to reduce inference complexity in Bayesian networks.

Connected through hyperedge HE-1 (induced by source paper "learning bayesian networks with restricted causal interactions"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "bayesian learning of loglinear models for neural connectivity"
    - "structure and parameter learning for causal independence and causal interaction models"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "context specific independence in bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "inference with causal independence in the cpsc network"):
  with members:
    - "causal independence for knowledge acquisition and inference"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-3 (induced by source paper "a differential semantics of lazy ar propagation"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "cautious propagation in bayesian networks"
    - "an empirical evaluation of possible variations of lazy propagation"

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)