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: "parameter learning of logic programs for symbolic statistical modeling"
  Abstract: We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks.#R##N##R##N#We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs.#R##N##R##N#The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.

Connected through hyperedge HE-1 (induced by source paper "value of information in probabilistic logic programs"):
  with members:
    - "an anytime algorithm for decision making under uncertainty"
    - "myopic value of information in influence diagrams"
    - "an approximate nonmyopic computation for value of information"
    - "near optimal nonmyopic value of information in graphical models"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "optimal value of information in graphical models"
    - "reasoning and planning with sensing actions incomplete information and static causal laws using answer set programming"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

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"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"

Connected through hyperedge HE-3 (induced by source paper "probabilistic logic programming with beta distributed random variables"):
  with members:
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-4 (induced by source paper "on the semantics and complexity of probabilistic logic programs"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "a new approach to updating beliefs"
    - "continuous time bayesian networks"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-5 (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-6 (induced by source paper "structure learning of probabilistic logic programs by searching the clause space"):
  with members:
    - "the bayesian structural em algorithm"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "markov logic networks for natural language question answering"):
  with members:
    - "map complexity results and approximation methods"
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-8 (induced by source paper "stochastic and or grammars a unified framework and logic perspective"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "joint video and text parsing for understanding events and answering queries"
    - "sum product networks a new deep architecture"

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)