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: "understanding and extending incremental determinization for 2qbf"
  Abstract: Incremental determinization is a recently proposed algorithm for solving quantified Boolean formulas with one quantifier alternation. In this paper, we formalize incremental determinization as a set of inference rules to help understand the design space of similar algorithms. We then present additional inference rules that extend incremental determinization in two ways. The first extension integrates the popular CEGAR principle and the second extension allows us to analyze different cases in isolation. The experimental evaluation demonstrates that the extensions significantly improve the performance.

Connected through hyperedge HE-1 (induced by source paper "learning heuristics for quantified boolean formulas through deep reinforcement learning"):
  with members:
    - "graph representations for higher order logic and theorem proving"
    - "learning a sat solver from single bit supervision"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "encodings of bounded synthesis"
    - "deep network guided proof search"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-2 (induced by source paper "knowledge compilation for boolean functional synthesis"):
  with members:
    - "what s hard about boolean functional synthesis"
    - "functional synthesis via input output separation"
    - "towards parallel boolean functional synthesis"
    - "skolem functions for factored formulas"
    - "propositional independence formula variable independence and forgetting"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "graph neural reasoning for 2 quantified boolean formula solvers"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "learning a sat solver from single bit supervision"

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)