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 to generate a good word embedding"):
  Abstract: We analyze three critical components of word embedding training: the model, the corpus, and the training parameters. We systematize existing neural-network-based word embedding algorithms and compare them using the same corpus. We evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks and using it to initialize neural networks. We also provide several simple guidelines for training word embeddings. First, we discover that corpus domain is more important than corpus size. We recommend choosing a corpus in a suitable domain for the desired task, after that, using a larger corpus yields better results. Second, we find that faster models provide sufficient performance in most cases, and more complex models can be used if the training corpus is sufficiently large. Third, the early stopping metric for iterating should rely on the development set of the desired task rather than the validation loss of training embedding.

Includes members:
  Member "distributed representations for compositional semantics":
    also connected through other hyperedges:
      (none)
  Member "evaluating neural word representations in tensor based compositional settings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep image to video adaptation and fusion networks for action recognition")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "word emdeddings through hellinger pca":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "holodetect few shot learning for error detection")
  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")
  Member "natural language processing almost from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "from frequency to meaning vector space models of semantics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)