human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "improved separations of regular resolution from clause learning proof systems"):
  Abstract: We prove that the graph tautology formulas of Alekhnovich, Johannsen, Pitassi, and Urquhart have polynomial size pool resolution refutations that use only input lemmas as learned clauses and without degenerate resolution inferences. We also prove that these graph tautology formulas can be refuted by polynomial size DPLL proofs with clause learning, even when restricted to greedy, unit-propagating DPLL search. We prove similar results for the guarded, xor-fied pebbling tautologies which Urquhart proved are hard for regular resolution.

Includes members:
  Member "small stone in pool":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards verifying nonlinear integer arithmetic")
  Member "an improved separation of regular resolution from pool resolution and clause learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards verifying nonlinear integer arithmetic")
  Member "clause learning algorithms with many restarts and bounded width resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a complexity theoretic understanding of restarts in sat solvers")
  Member "resolution trees with lemmas resolution refinements that characterize dll algorithms with clause learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on cdcl based proof systems with the ordered decision strategy")
  Member "towards understanding and harnessing the potential of clause learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on cdcl based proof systems with the ordered decision strategy")

Question: Please tell me which class the source paper 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)