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: "probabilistic deduction with conditional constraints over basic events"
  Abstract: We study the problem of probabilistic deduction with conditional constraints over basic events. We show that globally complete probabilistic deduction with conditional constraints over basic events is NP-hard. We then concentrate on the special case of probabilistic deduction in conditional constraint trees. We elaborate very efficient techniques for globally complete probabilistic deduction. In detail, for conditional constraint trees with point probabilities, we present a local approach to globally complete probabilistic deduction, which runs in linear time in the size of the conditional constraint trees. For conditional constraint trees with interval probabilities, we show that globally complete probabilistic deduction can be done in a global approach by solving nonlinear programs. We show how these nonlinear programs can be transformed into equivalent linear programs, which are solvable in polynomial time in the size of the conditional constraint trees.

Connected through hyperedge HE-1 (induced by source paper "probabilistic reasoning in the description logic alcp with the principle of maximum entropy full version"):
  with members:
    - "representation dependence in probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "credal networks under maximum entropy"):
  with members:
    - "coherent knowledge processing at maximum entropy by spirit"
    - "generating bayesian networks from probability logic knowledge bases"
    - "decision making with interval influence diagrams"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "from statistical knowledge bases to degrees of belief"
    - "probabilistic default reasoning with conditional constraints"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-3 (induced by source paper "consistency checking and querying in probabilistic databases under integrity constraints"):
  with members:
    - "probabilistic description logics"
    - "consensus answers for queries over probabilistic databases"
    - "conditioning probabilistic databases"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-4 (induced by source paper "magic inference rules for probabilistic deduction under taxonomic knowledge"):
  with members:
    - "constraint propagation with imprecise conditional probabilities"
    - "probabilistic description logics"
    - "a symbolic approach to reasoning with linguistic quantifiers"

Connected through hyperedge HE-5 (induced by source paper "deductive algorithmic knowledge"):
  with members:
    - "probabilistic algorithmic knowledge"
    - "on a theory of probabilistic deductive databases"

Connected through hyperedge HE-6 (induced by source paper "nonmonotonic probabilistic logics between model theoretic probabilistic logic and probabilistic logic under coherence"):
  with members:
    - "constraint propagation with imprecise conditional probabilities"
    - "probabilistic description logics"
    - "belief functions and default reasoning"
    - "probabilistic logic programming under inheritance with overriding"
    - "from statistical knowledge bases to degrees of belief"
    - "what does a conditional knowledge base entail"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-7 (induced by source paper "embedding default logic in propositional argumentation systems"):
  with members:
    - "belief functions and default reasoning"
    - "probabilistic assumption based reasoning"

Connected through hyperedge HE-8 (induced by source paper "parameter learning of logic programs for symbolic statistical modeling"):
  with members:
    - "loglinear models for first order probabilistic reasoning"
    - "from statistical knowledge bases to degrees of belief"
    - "linear tabulated resolution based on prolog control strategy"
    - "inside outside estimation of a lexicalized pcfg for german"
    - "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)