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 good old davis putnam procedure helps counting models"
  Abstract: As was shown recently, many important AI problems require counting the number of models of propositional formulas. The problem of counting models of such formulas is, according to present knowledge, computationally intractable in a worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP, that computes the exact number of models of a propositional CNF or DNF formula F. Let m and n be the number of clauses and variables of F, respectively, and let p denote the probability that a literal l of F occurs in a clause C of F, then the average running time of CDP is shown to be O(mdn), where d=[-1/log2(1-p)].The practical performance of CDP has been estimated in a series of experiments on a wide variety of CNF formulas.

Connected through hyperedge HE-1 (induced by source paper "estimating the density of states of boolean satisfiability problems on classical and quantum computing platforms"):
  with members:
    - "how biased is your model concentration inequalities information and model bias"
    - "relaxed survey propagation for the weighted maximum satisfiability problem"
    - "exact phase transitions in random constraint satisfaction problems"

Connected through hyperedge HE-2 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "structured learning modulo theories"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"

Connected through hyperedge HE-3 (induced by source paper "high dimensional discrete integration over the hypergrid"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "approximating the partition function of the ferromagnetic potts model"
    - "an approximation algorithm for k sat"

Connected through hyperedge HE-4 (induced by source paper "bit vector model counting using statistical estimation"):
  with members:
    - "combining the k cnf and xor phase transitions"
    - "the hard problems are almost everywhere for random cnf xor formulas"
    - "sound probabilistic sat with projection"
    - "approximate probabilistic inference via word level counting"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "a scalable approximate model counter"

Connected through hyperedge HE-5 (induced by source paper "constrained sampling and counting universal hashing meets sat solving"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-6 (induced by source paper "on the relationship between sum product networks and bayesian networks"):
  with members:
    - "big learning with bayesian methods"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "sum product networks a new deep architecture"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-7 (induced by source paper "model counting modulo theories"):
  with members:
    - "on bounding problems of quantitative information flow"
    - "pkind a parallel k induction based model checker"
    - "tarmo a framework for parallelized bounded model checking"

Connected through hyperedge HE-8 (induced by source paper "sampling techniques for boolean satisfiability"):
  with members:
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "probabilistic planning via heuristic forward search and weighted model counting"

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)