human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "riemannian optimization for skip gram negative sampling"):
  with members:
    - "towards a better understanding of predict and count models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"
    - "fixed rank matrix factorizations and riemannian low rank optimization"

Connected through hyperedge HE-2 (induced by source paper "representation learning models for entity search"):
  with members:
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "applying deep learning to answer selection a study and an open task"
    - "lstm based deep learning models for non factoid answer selection"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-3 (induced by source paper "the role of context types and dimensionality in learning word embeddings"):
  with members:
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "substitute based scode word embeddings in supervised nlp tasks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "intrinsic subspace evaluation of word embedding representations"):
  with members:
    - "do multi sense embeddings improve natural language understanding"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

Question: Please tell me which class the center node 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)