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: "the magic of logical inference in probabilistic programming"
  Abstract: Today, many different probabilistic programming languages exist and even more inference mechanisms for these languages. Still, most logic programming based languages use backward reasoning based on SLD resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions, nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. To achieve efficiency, we integrate two logic programming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent with the evidence.

Connected through hyperedge HE-1 (induced by source paper "semantic relational object tracking"):
  with members:
    - "simple does it weakly supervised instance and semantic segmentation"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "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"

Connected through hyperedge HE-2 (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"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-3 (induced by source paper "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"):
  with members:
    - "estimating mutual information for discrete continuous mixtures"
    - "design and implementation of probabilistic programming language anglican"
    - "inference compilation and universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "lifted inference for relational continuous models"

Connected through hyperedge HE-4 (induced by source paper "probabilistic planning by probabilistic programming"):
  with members:
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "probabilistic planning via heuristic forward search and weighted model counting"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-5 (induced by source paper "structured learning modulo theories"):
  with members:
    - "optimization modulo theories with linear rational costs"
    - "pushing the envelope of optimization modulo theories with linear arithmetic cost functions"
    - "probabilistic similarity logic"
    - "church a language for generative models"
    - "inference in probabilistic logic programs with continuous random variables"
    - "lifted relational variational inference"
    - "optimization in smt with la q cost functions"
    - "lifted inference for relational continuous models"

Connected through hyperedge HE-6 (induced by source paper "a logic programming approach to integration network inference"):
  with members:
    - "approximating constraint propagation in datalog"
    - "a path algebra for multi relational graphs"

Connected through hyperedge HE-7 (induced by source paper "model checking with probabilistic tabled logic programming"):
  with members:
    - "inference in probabilistic logic programs with continuous random variables"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "model checking probabilistic pushdown automata"
    - "bayesian logic programs"

Connected through hyperedge HE-8 (induced by source paper "inference in probabilistic logic programs with continuous random variables"):
  with members:
    - "parameter learning in prism programs with continuous random variables"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "bayesian logic programs"

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.LO(Logic in Computer Science)