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 description logics"
  Abstract: On the one hand, classical terminological knowledge representation excludes the possibility of handling uncertain concept descriptions involving, e.g., "usually true" concept properties, generalized quantifiers, or exceptions. On the other hand, purely numerical approaches for handling uncertainty in general are unable to consider terminological knowledge. This paper presents the language ACP which is a probabilistic extension of terminological logics and aims at closing the gap between the two areas of research. We present the formal semantics underlying the language ALUP and introduce the probabilistic formalism that is based on classes of probabilities and is realized by means of probabilistic constraints. Besides inferring implicitly existent probabilistic relationships, the constraints guarantee terminological and probabilistic consistency. Altogether, the new language ALUP applies to domains where both term descriptions and uncertainty have to be handled.

Connected through hyperedge HE-1 (induced by source paper "probabilistic dl reasoning with pinpointing formulas a prolog based approach"):
  with members:
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "on the implementation of the probabilistic logic programming language problog"
    - "efficient description logic reasoning in prolog the dlog system"
    - "on a theory of probabilistic deductive databases"

Connected through hyperedge HE-2 (induced by source paper "on constrained open world probabilistic databases"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "the dichotomy of conjunctive queries on probabilistic structures"

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

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"
    - "a symbolic approach to reasoning with linguistic quantifiers"
    - "probabilistic deduction with conditional constraints over basic events"

Connected through hyperedge HE-5 (induced by source paper "complexity analysis and variational inference for interpretation based probabilistic description logic"):
  with members:
    - "relational bayesian networks"
    - "the complexity of reasoning with cardinality restrictions and nominals in expressive description logics"

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"
    - "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"
    - "probabilistic default reasoning with conditional constraints"

Connected through hyperedge HE-7 (induced by source paper "reasoning within fuzzy description logics"):
  with members:
    - "an alternative proof method for possibilistic logic and its application to terminological logics"
    - "decidable reasoning in terminological knowledge representation systems"

Connected through hyperedge HE-8 (induced by source paper "probabilistic default reasoning with conditional constraints"):
  with members:
    - "generating new beliefs from old"
    - "constraint propagation with imprecise conditional probabilities"
    - "belief functions and default reasoning"
    - "a modification to evidential probability"
    - "probabilistic logic programming under inheritance with overriding"
    - "computing reference classes"
    - "from statistical knowledge bases to degrees of belief"
    - "what does a conditional knowledge base entail"

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)