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: "wordrep a benchmark for research on learning word representations"
  Abstract: WordRep is a benchmark collection for the research on learning distributed word representations (or word embeddings), released by Microsoft Research. In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding. Specifically, we describe how the evaluation tasks in WordRep are selected, how the data are sampled, and how the evaluation tool is built. We then compare several state-of-the-art word representations on WordRep, report their evaluation performance, and make discussions on the results. After that, we discuss new potential research topics that can be supported by WordRep, in addition to algorithm comparison. We hope that this paper can help people gain deeper understanding of WordRep, and enable more interesting research on learning distributed word representations and related topics.

Connected through hyperedge HE-1 (induced by source paper "evaluation of greek word embeddings"):
  with members:
    - "word embeddings from large scale greek web content"
    - "learning word vectors for 157 languages"
    - "evaluation of croatian word embeddings"
    - "enriching word vectors with subword information"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "a survey of word embeddings evaluation methods"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "an exploration of word embedding initialization in deep learning tasks"
    - "hypothesis testing based intrinsic evaluation of word embeddings"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "automated detection of non relevant posts on the russian imageboard 2ch importance of the choice of word representations"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-3 (induced by source paper "semi supervised multi task word embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "meta embedding as auxiliary task regularization"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-5 (induced by source paper "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"):
  with members:
    - "expanding subjective lexicons for social media mining with embedding subspaces"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "creating causal embeddings for question answering with minimal supervision"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "non distributional word vector representations"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-6 (induced by source paper "solving verbal comprehension questions in iq test by knowledge powered word embedding"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "incorporating both distributional and relational semantics in word representations"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "distributed representations of words and phrases and their compositionality"
    - "automated word puzzle generation via topic dictionaries"
    - "natural language processing almost from scratch"

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)