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: "stable model counting and its application in probabilistic logic programming"
  Abstract: Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.

Connected through hyperedge HE-1 (induced by source paper "semiring programming a declarative framework for generalized sum product problems"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "recursive decomposition for nonconvex optimization"
    - "the sum product theorem a foundation for learning tractable models"
    - "optimization modulo theories with linear rational costs"
    - "on the complexity of optimization problems based on compiled nnf representations"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "algebraic model counting"
    - "inference in probabilistic logic programs using weighted cnf s"

Connected through hyperedge HE-2 (induced by source paper "towards efficient discrete integration via adaptive quantile queries"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-3 (induced by source paper "adawish faster discrete integration via adaptive quantiles"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "closing the gap between short and long xors for model counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-4 (induced by source paper "implementing a relevance tracker module"):
  with members:
    - "lazy model expansion interleaving grounding with search"

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)