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: "rehabilitation of count based models for word vector representations"
  Abstract: Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarties as well as semantic and syntactic regularities. Instead, we aim at reviving interest in a model based on counts. We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurence statistics of large text corpora. We show that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation. Besides being both simple and intuitive, this method also provides an encoding function which can be used to infer unseen words or phrases. This becomes a clear advantage compared to predictive models which must train these new words.

Connected through hyperedge HE-1 (induced by source paper "wordnet2vec corpora agnostic word vectorization method"):
  with members:
    - "text understanding from scratch"
    - "distributed representations of sentences and documents"
    - "fast and accurate sentiment classification using an enhanced naive bayes model"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "distributed representations of words and phrases and their compositionality"
    - "two step cca a new spectral method for estimating vector models of words"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "phrase based image captioning"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "explain images with multimodal recurrent neural networks"
    - "translating videos to natural language using deep recurrent neural networks"
    - "learning a recurrent visual representation for image caption generation"

Connected through hyperedge HE-3 (induced by source paper "simple image description generator via a linear phrase based approach"):
  with members:
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "return of the devil in the details delving deep into convolutional nets"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "explain images with multimodal recurrent neural networks"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"
    - "show and tell a neural image caption generator"

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)