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 "learning numeral embeddings"):
  Abstract: Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to effectively embed numerals, a special subset of words, is still underexplored. Existing word embedding methods do not learn numeral embeddings well because there are an infinite number of numerals and their individual appearances in training corpora are highly scarce. In this paper, we propose two novel numeral embedding methods that can handle the out-of-vocabulary (OOV) problem for numerals. We first induce a finite set of prototype numerals using either a self-organizing map or a Gaussian mixture model. We then represent the embedding of a numeral as a weighted average of the prototype number embeddings. Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training. We evaluated our methods and showed its effectiveness on four intrinsic and extrinsic tasks: word similarity, embedding numeracy, numeral prediction, and sequence labeling.

Includes members:
  Member "do nlp models know numbers probing numeracy in embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "olmpics on what language model pre training captures")
  Member "numeracy for language models evaluating and improving their ability to predict numbers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do nlp models know numbers probing numeracy in embeddings")
  Member "ncrf an open source neural sequence labeling toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parsing as pretraining")
  Member "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study")
  Member "numerically grounded language models for semantic error correction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "just add functions a neural symbolic language model")
  Member "solving general arithmetic word problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference")
  Member "simlex 999 evaluating semantic models with genuine similarity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual 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)