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 "transformers as soft reasoners over language"):
  Abstract: AI has long pursued the goal of having systems reason over *explicitly provided* knowledge, but building suitable representations has proved challenging. Here we explore whether transformers can similarly learn to reason (or emulate reasoning), but using rules expressed in language, thus bypassing a formal representation. We provide the first demonstration that this is possible, and characterize the extent of this capability. To do this, we use a collection of synthetic datasets that test increasing levels of reasoning complexity (number of rules, presence of negation, and depth of chaining). We find transformers appear to learn rule-based reasoning with high (99%) accuracy on these datasets, and in a way that generalizes to test data requiring substantially deeper chaining than in the training data (95%+ scores). We also demonstrate that the models transfer well to two hand-authored rulebases, and to rulebases paraphrased into more natural language. These findings are significant as it suggests a new role for transformers, namely as a limited "soft theorem prover" operating over explicit theories in language. This in turn suggests new possibilities for explainability, correctability, and counterfactual reasoning in question-answering. All datasets and a live demo are available at this http URL

Includes members:
  Member "differentiable reasoning on large knowledge bases and natural language":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence")
  Member "learning deep transformer models for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "nlprolog reasoning with weak unification for question answering in natural language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable reasoning on large knowledge bases and natural language")
  Member "probing natural language inference models through semantic fragments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "olmpics on what language model pre training captures")
  Member "improving graph neural network representations of logical formulae with subgraph pooling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic")
  Member "analysing mathematical reasoning abilities of neural models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaching temporal logics to neural networks")
  Member "establishing strong baselines for the new decade sequence tagging syntactic and semantic parsing with bert":
    also connected through other hyperedges:
      (none)
  Member "quartz an open domain dataset of qualitative relationship questions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

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.CL(Computation and Language)