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 learning algorithms with many restarts and bounded width resolution"
  Abstract: We offer a new understanding of some aspects of practical SAT-solvers that are based on DPLL with unit-clause propagation, clause-learning, and restarts. We do so by analyzing a concrete algorithm which we claim is faithful to what practical solvers do. In particular, before making any new decision or restart, the solver repeatedly applies the unit-resolution rule until saturation, and leaves no component to the mercy of non-determinism except for some internal randomness. We prove the perhaps surprising fact that, although the solver is not explicitly designed for it, with high probability it ends up behaving as width-k resolution after no more than O(n2k+2) conflicts and restarts, where n is the number of variables. In other words, width-k resolution can be thought of as O(n2k+2) restarts of the unit-resolution rule with learning.

Connected through hyperedge HE-1 (induced by source paper "towards a complexity theoretic understanding of restarts in sat solvers"):
  with members:
    - "on cdcl based proof systems with the ordered decision strategy"
    - "improved separations of regular resolution from clause learning proof systems"

Connected through hyperedge HE-2 (induced by source paper "constructing hard examples for graph isomorphism"):
  with members:
    - "definable inapproximability new challenges for duplicator"
    - "an exponential lower bound for individualization refinement algorithms for graph isomorphism"
    - "the pebbling comonad in finite model theory"
    - "benchmark graphs for practical graph isomorphism"
    - "graph isomorphism in quasipolynomial time"
    - "lower bounds for existential pebble games and k consistency tests"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-3 (induced by source paper "understanding vsids branching heuristics in conflict driven clause learning sat solvers"):
  with members:
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-4 (induced by source paper "narrow proofs may be maximally long"):
  with members:
    - "a generalized method for proving polynomial calculus degree lower bounds"
    - "tight size degree bounds for sums of squares proofs"
    - "communication lower bounds via critical block sensitivity"
    - "on the complexity of finding narrow proofs"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "understanding space in proof complexity separations and trade offs via substitutions"

Connected through hyperedge HE-5 (induced by source paper "proof complexity modulo the polynomial hierarchy understanding alternation as a source of hardness"):
  with members:
    - "beyond q resolution and prenex form a proof system for quantified constraint satisfaction"
    - "on qbf proofs and preprocessing"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-6 (induced by source paper "local consistency and sat solvers"):
  with members:
    - "arc consistency and friends"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-7 (induced by source paper "pebble games proof complexity and time space trade offs"):
  with members:
    - "understanding space in proof complexity separations and trade offs via substitutions"
    - "on the relative strength of pebbling and resolution"
    - "towards an optimal separation of space and length in resolution"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-8 (induced by source paper "the complexity of proving that a graph is ramsey"):
  with members:
    - "linear logic based analysis of constraint handling rules with disjunction"

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)