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: "clp bn constraint logic programming for probabilistic knowledge"
  Abstract: We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constraint logic programming framework. Arguably, an important limitation of traditional Bayesian networks is that they are propositional, and thus cannot represent relations between multiple similar objects in multiple contexts. Several researchers have thus proposed first-order languages to describe such networks. Namely, one very successful example of this approach are the Probabilistic Relational Models (PRMs), that combine Bayesian networks with relational database technology. The key difficulty that we had to address when designing CLP(cal{BN}) is that logic based representations use ground terms to denote objects. With probabilitic data, we need to be able to uniquely represent an object whose value we are not sure about. We use {sl Skolem functions} as unique new symbols that uniquely represent objects with unknown value. The semantics of CLP(cal{BN}) programs then naturally follow from the general framework of constraint logic programming, as applied to a specific domain where we have probabilistic data. This paper introduces and defines CLP(cal{BN}), and it describes an implementation and initial experiments. The paper also shows how CLP(cal{BN}) relates to Probabilistic Relational Models (PRMs), Ngo and Haddawys Probabilistic Logic Programs, AND Kersting AND De Raedts Bayesian Logic Programs.

Connected through hyperedge HE-1 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "improving knowledge graph embedding using simple constraints"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-2 (induced by source paper "constraint based inference in probabilistic logic programs"):
  with members:
    - "inference in probabilistic logic programs using lifted explanations"
    - "new liftable classes for first order probabilistic inference"
    - "lifted variable elimination for probabilistic logic programming"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "stochastic logic programs sampling inference and applications"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "logical hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "lower complexity bounds for lifted inference"
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"

Connected through hyperedge HE-4 (induced by source paper "structure learning of probabilistic logic programs by searching the clause space"):
  with members:
    - "the bayesian structural em algorithm"
    - "unachievable region in precision recall space and its effect on empirical evaluation"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "on the implementation of the probabilistic logic programming language problog"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-5 (induced by source paper "inferring user preferences by probabilistic logical reasoning over social networks"):
  with members:
    - "timeline generation tracking individuals on twitter"
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "discriminative probabilistic models for relational data"
    - "probabilistic similarity logic"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-6 (induced by source paper "probabilistic programming concepts"):
  with members:
    - "relational bayesian networks"
    - "church a language for generative models"
    - "gibbs sampling in open universe stochastic languages"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-7 (induced by source paper "model checking with probabilistic tabled logic programming"):
  with members:
    - "inference in probabilistic logic programs with continuous random variables"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "the magic of logical inference in probabilistic programming"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "model checking probabilistic pushdown automata"
    - "bayesian logic programs"

Connected through hyperedge HE-8 (induced by source paper "sampling first order logical particles"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "logical hidden markov models"
    - "reasoning about noisy sensors and effectors in the situation calculus"

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)