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: "penalty logic and its link with dempster shafer theory"
  Abstract: Penalty logic, introduced by Pinkas, associates to each formula of a knowledge base the price to pay if this formula is violated. Penalties may be used as a criterion for selecting preferred consistent subsets in an inconsistent knowledge base, thus inducing a non-monotonic inference relation. A precise formalization and the main properties of penalty logic and of its associated non-monotonic inference relation are given in the first part. We also show that penalty logic and Dempster-Shafer theory are related, especially in the infinitesimal case.

Connected through hyperedge HE-1 (induced by source paper "human in the loop learning of qualitative preference models"):
  with members:
    - "possibilistic constraint satisfaction problems or how to handle soft constraints"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-2 (induced by source paper "learning optimal and near optimal lexicographic preference lists"):
  with members:
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-3 (induced by source paper "learning possibilistic logic theories from default rules"):
  with members:
    - "what does a conditional knowledge base entail"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-4 (induced by source paper "encoding markov logic networks in possibilistic logic"):
  with members:
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-5 (induced by source paper "belief functions and default reasoning"):
  with members:
    - "generalized qualitative probability savage revisited"
    - "defaults and infinitesimals defeasible inference by nonarchimedean entropy maximization"
    - "syntax based default reasoning as probabilistic model based diagnosis"
    - "default reasoning and the transferable belief model"
    - "what does a conditional knowledge base entail"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"

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)