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: "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
  Abstract: We suggest a new method for creating and using gold-standard datasets for word similarity evaluation. Our goal is to improve the reliability of the evaluation, and we do this by redesigning the annotation task to achieve higher inter-rater agreement, and by defining a performance measure which takes the reliability of each annotation decision in the dataset into account.

Connected through hyperedge HE-1 (induced by source paper "syntactic interchangeability in word embedding models"):
  with members:
    - "a survey of word embeddings evaluation methods"
    - "concept2vec metrics for evaluating quality of embeddings for ontological concepts"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "redefining context windows for word embedding models an experimental study"
    - "enriching word vectors with subword information"
    - "unsupervised pos induction with word embeddings"
    - "a primer on neural network models for natural language processing"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

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 "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"
    - "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"
    - "wordrep a benchmark for research on learning word representations"

Connected through hyperedge HE-4 (induced by source paper "size vs structure in training corpora for word embedding models araneum russicum maximum and russian national corpus"):
  with members:
    - "texts in meaning out neural language models in semantic similarity task for russian"
    - "a primer on neural network models for natural language processing"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"
    - "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)