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 "elementary logic in linear space"):
  Abstract: First-order logic is typically presented as the study of deduction in a setting with elementary quantification. In this paper, we take another vantage point and conceptualize first-order logic as a linear space that encodes "plausibility". Whereas a deductive perspective emphasizes how (i.e., process), a space perspective emphasizes where (i.e., location). We explore several consequences that a shift in perspective to "signals in space" has for first-order logic, including (1) a notion of proof based on orthogonal decomposition, (2) a method for assigning probabilities to sentences that reflects logical uncertainty, and (3) a "models as boundary" principle that relates the models of a theory to its "size".

Includes members:
  Member "on learning to prove":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to prove theorems by learning to generate theorems")
  Member "learning a sat solver from single bit supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "teaching temporal logics to neural networks")
  Member "gamepad a learning environment for theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards finding longer proofs")
  Member "reinforcement learning of theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic")
  Member "deep network guided proof search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "teaching temporal logics to neural networks")
  Member "holstep a machine learning dataset for higher order logic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards finding longer proofs")
  Member "analysis of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "machine learning of coq proof guidance first experiments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "holist an environment for machine learning of higher order theorem proving")

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)