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 and learning in probabilistic logic programs using weighted boolean formulas"
  Abstract: Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before. The rst contribution of this paper is a suite of ecient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

Connected through hyperedge HE-1 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence"):
  with members:
    - "relational neural machines"
    - "neural logic machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "playgol learning programs through play"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"

Connected through hyperedge HE-2 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "geodesic walks in polytopes"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "probabilistic theorem proving"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-3 (induced by source paper "semantic relational object tracking"):
  with members:
    - "simple does it weakly supervised instance and semantic segmentation"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"
    - "learning human activities and object affordances from rgb d videos"
    - "the magic of logical inference in probabilistic programming"

Connected through hyperedge HE-4 (induced by source paper "a critical look at the applicability of markov logic networks for music signal analysis"):
  with members:
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "expander cnfs have exponential dnnf size"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-6 (induced by source paper "compiling stochastic constraint programs to and or decision diagrams"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "stochastic constraint programming"
    - "solving sat and bayesian inference with backtracking search"
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "stochastic constraint programming a scenario based approach"

Connected through hyperedge HE-7 (induced by source paper "symbolic exact inference for discrete probabilistic programs"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "measure transformer semantics for bayesian machine learning"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "a knowledge compilation map"

Connected through hyperedge HE-8 (induced by source paper "an automated engineering assistant learning parsers for technical drawings"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "deepproblog neural probabilistic logic programming"
    - "deep residual learning for image recognition"

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)