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: "redefining context windows for word embedding models an experimental study"
  Abstract: Distributional semantic models learn vector representations of words through the contexts they occur in. Although the choice of context (which often takes the form of a sliding window) has a direct influence on the resulting embeddings, the exact role of this model component is still not fully understood. This paper presents a systematic analysis of context windows based on a set of four distinct hyper-parameters. We train continuous Skip-Gram models on two English-language corpora for various combinations of these hyper-parameters, and evaluate them on both lexical similarity and analogy tasks. Notable experimental results are the positive impact of cross-sentential contexts and the surprisingly good performance of right-context windows.

Connected through hyperedge HE-1 (induced by source paper "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  with members:
    - "high risk learning acquiring new word vectors from tiny data"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "issues in evaluating semantic spaces using word analogies"
    - "mining and modeling character networks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (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"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "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"

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)