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 "learning heuristics for quantified boolean formulas through deep reinforcement learning"):
  Abstract: We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.

Includes members:
  Member "graph representations for higher order logic and theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks")
  Member "understanding and extending incremental determinization for 2qbf":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge compilation for boolean functional synthesis")
  Member "learning a sat solver from single bit supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "teaching temporal logics to neural networks")
  Member "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep reinforcement learning based approach to learning transferable proof guidance strategies")
  Member "can neural networks understand logical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards finding longer proofs")
  Member "reinforcement learning of theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic")
  Member "encodings of bounded synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fbsat automatic inference of minimal finite state models of function blocks using sat solver")
  Member "deep network guided proof search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "teaching temporal logics to neural networks")

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)