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: "causal independence for knowledge acquisition and inference"
  Abstract: I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief-network representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications.

Connected through hyperedge HE-1 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "value directed belief state approximation for pomdps"

Connected through hyperedge HE-2 (induced by source paper "independence of causal influence and clique tree propagation"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "the use of conflicts in searching bayesian networks"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "network fragments representing knowledge for constructing probabilistic models"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "model based influence diagrams for machine vision"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "object oriented bayesian networks"
    - "utility based control for computer vision"

Connected through hyperedge HE-4 (induced by source paper "context specific independence in bayesian networks"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "on heuristics for finding loop cutsets in multiply connected belief networks"
    - "learning bayesian networks with local structure"
    - "a probabilistic calculus of actions"
    - "advances in probabilistic reasoning"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"

Connected through hyperedge HE-5 (induced by source paper "inference with causal independence in the cpsc network"):
  with members:
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "inter causal independence and heterogeneous factorization"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-6 (induced by source paper "diagnosis of multiple faults a sensitivity analysis"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "probabilistic interpretations for mycin s certainty factors"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-7 (induced by source paper "exploiting functional dependence in bayesian network inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "multiplicative factorization of noisy max"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "lazy propagation in junction trees"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-8 (induced by source paper "exploiting structure in weighted model counting approaches to probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "multiplicative factorization of noisy max"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "context specific independence in bayesian networks"
    - "exploiting evidence in probabilistic inference"
    - "exploiting causal independence in bayesian network inference"

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)