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: "approximate counting in smt and value estimation for probabilistic programs"
  Abstract: #SMT, or model counting for logical theories, is a well-known hard problem that generalizes such tasks as counting the number of satisfying assignments to a Boolean formula and computing the volume of a polytope. In the realm of satisfiability modulo theories (SMT) there is a growing need for model counting solvers, coming from several application domains (quantitative information flow, static analysis of probabilistic programs). In this paper, we show a reduction from an approximate version of #SMT to SMT. #R##N#We focus on the theories of integer arithmetic and linear real arithmetic. We propose model counting algorithms that provide approximate solutions with formal bounds on the approximation error. They run in polynomial time and make a polynomial number of queries to the SMT solver for the underlying theory, exploiting "for free" the sophisticated heuristics implemented within modern SMT solvers. We have implemented the algorithms and used them to solve the value problem for a model of loop-free probabilistic programs with nondeterminism.

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"
    - "algebraic model counting"
    - "inference in probabilistic logic programs using weighted cnf s"

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"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "distribution aware sampling and weighted model counting for sat"
    - "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 "smt ilp"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"

Connected through hyperedge HE-4 (induced by source paper "the complexity of verifying loop free programs as differentially private"):
  with members:
    - "automated methods for checking differential privacy"
    - "property testing for differential privacy"
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"

Connected through hyperedge HE-5 (induced by source paper "efficient search based weighted model integration"):
  with members:
    - "probabilistic inference modulo theories"
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-6 (induced by source paper "mcpa program analysis as machine learning"):
  with members:
    - "assurances in software testing a roadmap"
    - "statistical model checking an overview"

Connected through hyperedge HE-7 (induced by source paper "trace abstraction modulo probability"):
  with members:
    - "an assertion based program logic for probabilistic programs"
    - "constraint based synthesis of coupling proofs"
    - "efficient interpolation for the theory of arrays"
    - "synthesizing coupling proofs of differential privacy"
    - "a new proof rule for almost sure termination"
    - "towards practical differential privacy for sql queries"
    - "a storm is coming a modern probabilistic model checker"
    - "a program logic for union bounds"

Connected through hyperedge HE-8 (induced by source paper "scaling up probabilistic inference in linear and non linear hybrid domains by leveraging knowledge compilation"):
  with members:
    - "quantifying program bias"
    - "probabilistic inference modulo theories"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "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"

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)