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 "how large are lions inducing distributions over quantitative attributes"):
  Abstract: Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as "Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.

Includes members:
  Member "numeracy for language models evaluating and improving their ability to predict numbers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do nlp models know numbers probing numeracy in embeddings")
  Member "robust handling of polysemy via sparse representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "improving temporal relation extraction with a globally acquired statistical resource":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "verb physics relative physical knowledge of actions and objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "how much is 131 million dollars putting numbers in perspective with compositional descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "textual analogy parsing what s shared and what s compared among analogous facts")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "numerically grounded language models for semantic error correction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "just add functions a neural symbolic language model")
  Member "are elephants bigger than butterflies reasoning about sizes of objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "olmpics on what language model pre training captures")

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)