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 "on the capabilities and limitations of reasoning for natural language understanding"):
  Abstract: Recent systems for natural language understanding are strong at overcoming linguistic variability for lookup style reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations, addressing the ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. Our formal model uses two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic symbol space that captures a noisy grounding of the meaning space in the symbols or words of a language. #R##N#We apply this framework to study the connectivity problem in undirected graphs---a core reasoning problem that forms the basis for more complex multi-hop reasoning. We show that it is indeed possible to construct a high-quality algorithm for detecting connectivity in the (latent) meaning graph, based on an observed noisy symbol graph, as long as the noise is below our quantified noise level and only a few hops are needed. On the other hand, we also prove an impossibility result: if a query requires a large number (specifically, logarithmic in the size of the meaning graph) of hops, no reasoning system operating over the symbol graph is likely to recover any useful property of the meaning graph. This highlights a fundamental barrier for a class of reasoning problems and systems, and suggests the need to limit the distance between the two spaces, rather than investing in multi-hop reasoning with "many" hops.

Includes members:
  Member "question answering as global reasoning over semantic abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bridging knowledge graphs to generate scene graphs")
  Member "multi hop knowledge graph reasoning with reward shaping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformers as soft reasoners over language")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "universal semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "answering complex questions using open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "question answering via integer programming over semi structured knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "chains of reasoning over entities relations and text using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings")

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)