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 "english out of vocabulary lexical evaluation task"):
  Abstract: Unlike previous unknown nouns tagging task (Curran, 2005) (Ciaramita and Johnson, 2003), this is the first attempt to focus on out-of- vocabulary(OOV) lexical evaluation tasks that does not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to pro- vide solutions for OOV lexical classification and predication. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we uti- lize unsupervised word embedding methods such as Word2Vec(Mikolov et al., 2013) and Word2GM (Athiwaratkun and Wilson, 2017) to perform the baseline experiments on the cat- egorical classification task and OOV words at- tribute prediction tasks.

Includes members:
  Member "an optimized system to solve text based captcha":
    also connected through other hyperedges:
      (none)
  Member "zero shot learning via semantic similarity embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zstad zero shot temporal activity detection")
  Member "word2vec explained deriving mikolov et al s negative sampling word embedding method":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "word representations via gaussian embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an inductive bias for distances neural nets that respect the triangle inequality")
  Member "distributed representations of words and phrases and their compositionality":
    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")

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)