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: "inference in probabilistic logic programs using weighted cnf s"
  Abstract: Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting. To solve such tasks, we employ state-of-the-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-the-art in probabilistic logic programming.

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"
    - "stable model counting and its application in probabilistic logic programming"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "context specific independence in bayesian networks"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-3 (induced by source paper "a study of the learnability of relational properties model counting meets machine learning"):
  with members:
    - "moet interpretable and verifiable reinforcement learning via mixture of expert trees"
    - "quantitative verification of neural networks and its security applications"
    - "symbolic execution for deep neural networks"
    - "concolic testing for deep neural networks"
    - "verifiable reinforcement learning via policy extraction"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "reasoning about identifier spaces how to make chord correct"

Connected through hyperedge HE-4 (induced by source paper "symbolic exact inference for discrete probabilistic programs"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "measure transformer semantics for bayesian machine learning"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "learning probabilistic logic programs in continuous domains"):
  with members:
    - "quantifying program bias"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "a variational approximation for bayesian networks with discrete and continuous latent variables"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "the magic of logical inference in probabilistic programming"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-6 (induced by source paper "tractable querying and learning in hybrid domains via sum product networks"):
  with members:
    - "quantifying program bias"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-7 (induced by source paper "semiring programming a framework for search inference and learning"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "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"
    - "church a language for generative models"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-8 (induced by source paper "a probabilistic logic programming event calculus"):
  with members:
    - "location based reasoning about complex multi agent behavior"
    - "on the implementation of the probabilistic logic programming language problog"
    - "a logic programming approach to activity recognition"
    - "knowledge representation concepts for automated sla management"
    - "eca ruleml an approach combining eca rules with temporal interval based kr event action logics and transactional update logics"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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)