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: "weight learning in a probabilistic extension of answer set programs"
  Abstract: LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.

Connected through hyperedge HE-1 (induced by source paper "strong equivalence for lpmln programs"):
  with members:
    - "a probabilistic extension of action language bc"
    - "computing lpmln using asp and mln solvers"
    - "logic tensor networks for semantic image interpretation"
    - "tensorlog a differentiable deductive database"
    - "probabilistic reasoning with answer sets"
    - "propositional theories are strongly equivalent to logic programs"
    - "diagnostic reasoning with a prolog"

Connected through hyperedge HE-2 (induced by source paper "on the strong equivalences for lpmln programs"):
  with members:
    - "asp core 2 input language format"
    - "computing lpmln using asp and mln solvers"
    - "probabilistic reasoning with answer sets"
    - "discovering classes of strongly equivalent logic programs"
    - "semantical characterizations and complexity of equivalences in answer set programming"
    - "logic programming with ordered disjunction"

Connected through hyperedge HE-3 (induced by source paper "differentiable satisfiability and differentiable answer set programming for sampling based multi model optimization"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "computing lpmln using asp and mln solvers"
    - "tensorlog a differentiable deductive database"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "distribution aware sampling and weighted model counting for sat"
    - "probabilistic inductive logic programming based on answer set programming"
    - "probabilistic reasoning with answer sets"

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)