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: "resolution trees with lemmas resolution refinements that characterize dll algorithms with clause learning"
  Abstract: Resolution refinements called w-resolution trees with lemmas (WRTL) and with#N#input lemmas (WRTI) are introduced. Dag-like resolution is equivalent to both#N#WRTL and WRTI when there is no regularity condition. For regular proofs, an#N#exponential separation between regular dag-like resolution and both regular#N#WRTL and regular WRTI is given.#N#  It is proved that DLL proof search algorithms that use clause learning based#N#on unit propagation can be polynomially simulated by regular WRTI. More#N#generally, non-greedy DLL algorithms with learning by unit propagation are#N#equivalent to regular WRTI. A general form of clause learning, called#N#DLL-Learn, is defined that is equivalent to regular WRTL.#N#  A variable extension method is used to give simulations of resolution by#N#regular WRTI, using a simplified form of proof trace extensions. DLL-Learn and#N#non-greedy DLL algorithms with learning by unit propagation can use variable#N#extensions to simulate general resolution without doing restarts.#N#  Finally, an exponential lower bound for WRTL where the lemmas are restricted#N#to short clauses is shown.

Connected through hyperedge HE-1 (induced by source paper "on cdcl based proof systems with the ordered decision strategy"):
  with members:
    - "understanding vsids branching heuristics in conflict driven clause learning sat solvers"
    - "improved separations of regular resolution from clause learning proof systems"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-2 (induced by source paper "small stone in pool"):
  with members:
    - "an improved separation of regular resolution from pool resolution and clause learning"
    - "improved separations of regular resolution from clause learning proof systems"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-3 (induced by source paper "improved separations of regular resolution from clause learning proof systems"):
  with members:
    - "small stone in pool"
    - "an improved separation of regular resolution from pool resolution and clause learning"
    - "clause learning algorithms with many restarts and bounded width resolution"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-4 (induced by source paper "an improved separation of regular resolution from pool resolution and clause learning"):
  with members:
    - "clause learning algorithms with many restarts and bounded width resolution"
    - "towards understanding and harnessing the potential of clause learning"

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)