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: "clause term resolution and learning in the evaluation of quantified boolean formulas"
  Abstract: Resolution is the rule of inference at the basis of most procedures for automated reasoning. In these procedures, the input formula is first translated into an equisatisfiable formula in conjunctive normal form (CNF) and then represented as a set of clauses. Deduction starts by inferring new clauses by resolution, and goes on until the empty clause is generated or satisfiability of the set of clauses is proven, e.g., because no new clauses can be generated.#R##N##R##N#In this paper, we restrict our attention to the problem of evaluating Quantified Boolean Formulas (QBFs). In this setting, the above outlined deduction process is known to be sound and complete if given a formula in CNF and if a form of resolution, called "Q-resolution", is used. We introduce Q-resolution on terms, to be used for formulas in disjunctive normal form. We show that the computation performed by most of the available procedures for QBFs -based on the Davis-Logemann-Loveland procedure (DLL) for propositional satisfiability- corresponds to a tree in which Q-resolution on terms and clauses alternate. This poses the theoretical bases for the introduction of learning, corresponding to recording Q-resolution formulas associated with the nodes of the tree. We discuss the problems related to the introduction of learning in DLL based procedures, and present solutions extending state-of-the-art proposals coming from the literature on propositional satisfiability. Finally, we show that our DLL based solver extended with learning, performs significantly better on benchmarks used in the 2003 QBF solvers comparative evaluation.

Connected through hyperedge HE-1 (induced by source paper "qratpre effective qbf preprocessing via strong redundancy properties"):
  with members:
    - "expansion based qbf solving without recursion"
    - "evaluating qbf solvers quantifier alternations matter"
    - "on expansion and resolution in cegar based qbf solving"
    - "textsf qrat generalizing qrat by a more powerful qbf redundancy property"
    - "depqbf 6 0 a search based qbf solver beyond traditional qcdcl"
    - "the qbf gallery"

Connected through hyperedge HE-2 (induced by source paper "evaluating qbf solvers quantifier alternations matter"):
  with members:
    - "on expansion and resolution in cegar based qbf solving"
    - "size cost and capacity a semantic technique for hard random qbfs"
    - "depqbf 6 0 a search based qbf solver beyond traditional qcdcl"
    - "the qbf gallery"
    - "proof complexity modulo the polynomial hierarchy understanding alternation as a source of hardness"
    - "meditations on quantified constraint satisfaction"

Connected through hyperedge HE-3 (induced by source paper "depqbf 6 0 a search based qbf solver beyond traditional qcdcl"):
  with members:
    - "evaluating qbf solvers quantifier alternations matter"
    - "the qbf gallery"
    - "q resolution with generalized axioms"
    - "incrementally computing minimal unsatisfiable cores of qbfs via a clause group solver api"
    - "conformant planning as a case study of incremental qbf solving"
    - "incremental qbf solving"
    - "computing resolution path dependencies in linear time"

Connected through hyperedge HE-4 (induced by source paper "the qbf gallery"):
  with members:
    - "conformant planning as a case study of incremental qbf solving"

Connected through hyperedge HE-5 (induced by source paper "hordeqbf a modular and massively parallel qbf solver"):
  with members:
    - "hordesat a massively parallel portfolio sat solver"

Connected through hyperedge HE-6 (induced by source paper "q resolution with generalized axioms"):
  with members:
    - "the qbf gallery"

Connected through hyperedge HE-7 (induced by source paper "incrementally computing minimal unsatisfiable cores of qbfs via a clause group solver api"):
  with members:
    - "conformant planning as a case study of incremental qbf solving"
    - "incremental qbf solving"
    - "a scalable algorithm for minimal unsatisfiable core extraction"

Connected through hyperedge HE-8 (induced by source paper "conformant planning as a case study of incremental qbf solving"):
  with members:
    - "incremental qbf solving"
    - "on qbf proofs and preprocessing"
    - "compiling uncertainty away in conformant planning problems with bounded width"

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)