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 "improving graph neural network representations of logical formulae with subgraph pooling"):
  Abstract: Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. In particular, there has been a shift from character and token-level representations to graph-structured representations, in large part driven by the rapidly emerging body of research on geometric deep learning. Typically, structure-aware neural methods for embedding logical formulae have been variants of either Tree LSTMs or GNNs. While more effective than character and token-level approaches, such methods have often made representational trade-offs that limited their ability to effectively represent the global structure of their inputs. In this work, we introduce a novel approach for embedding logical formulae using DAG LSTMs that is designed to overcome the limitations of both Tree LSTMs and GNNs. The effectiveness of the proposed framework is demonstrated on the tasks of premise selection and proof step classification where it achieves the state-of-the-art performance on two standard datasets.

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 "holist an environment for machine learning of higher order theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "teaching temporal logics to neural networks")
  Member "property invariant embedding for automated reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic")
  Member "gamepad a learning environment for theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "elementary logic in linear space")
  Member "can neural networks understand logical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards finding longer proofs")
  Member "premise selection with neural networks and distributed representation of features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "property invariant embedding for automated reasoning")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  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.AI(Artificial Intelligence)