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: "probabilistic inference modulo theories"
  Abstract: We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers). While many solutions to probabilistic inference over logic representations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean algorithms with constant complexity in the domain size (the number of values that variables can take). We also detail a solver for summations with difference arithmetic and show experimental results from a scenario in which SGDPLL(T) is much faster than a state-of-the-art probabilistic solver.

Connected through hyperedge HE-1 (induced by source paper "scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing"):
  with members:
    - "efficient search based weighted model integration"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "learning bayesian networks a unification for discrete and gaussian domains"

Connected through hyperedge HE-2 (induced by source paper "efficient search based weighted model integration"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-3 (induced by source paper "hybrid probabilistic inference with logical constraints tractability and message passing"):
  with members:
    - "efficient search based weighted model integration"
    - "learning bayesian networks a unification for discrete and gaussian domains"

Connected through hyperedge HE-4 (induced by source paper "from high level inference algorithms to efficient code"):
  with members:
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"
    - "swift compiled inference for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "dynamic blocking and collapsing for gibbs sampling"
    - "church a language for generative models"
    - "exploiting contextual independence in probabilistic inference"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "scaling up probabilistic inference in linear and non linear hybrid domains by leveraging knowledge compilation"):
  with members:
    - "quantifying program bias"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "algebraic model counting"
    - "symbolic dynamic programming for discrete and continuous state mdps"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"
    - "a knowledge compilation map"

Connected through hyperedge HE-6 (induced by source paper "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"):
  with members:
    - "model counting modulo theories"
    - "church a language for generative models"
    - "lifted variable elimination a novel operator and completeness results"

Connected through hyperedge HE-7 (induced by source paper "anytime exact belief propagation"):
  with members:
    - "accuracy bounds for belief propagation"
    - "bayesian logic programs"

Connected through hyperedge HE-8 (induced by source paper "approximate probabilistic inference via word level counting"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "sampling techniques for boolean satisfiability"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "structured learning modulo theories"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

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)