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: "two step cca a new spectral method for estimating vector models of words"
  Abstract: Unlabeled data is often used to learn representations which can be used to supplement baseline features in a supervised learner. For example, for text applications where the words lie in a very high dimensional space (the size of the vocabulary), one can learn a low rank "dictionary" by an eigen-decomposition of the word co-occurrence matrix (e.g. using PCA or CCA). In this paper, we present a new spectral method based on CCA to learn an eigenword dictionary. Our improved procedure computes two set of CCAs, the first one between the left and right contexts of the given word and the second one between the projections resulting from this CCA and the word itself. We prove theoretically that this two-step procedure has lower sample complexity than the simple single step procedure and also illustrate the empirical efficacy of our approach and the richness of representations learned by our Two Step CCA (TSCCA) procedure on the tasks of POS tagging and sentiment classification.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-2 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "representing sentences as low rank subspaces"):
  with members:
    - "geometry of polysemy"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "geometry of compositionality"
    - "learning distributed representations of sentences from unlabelled data"
    - "a latent variable model approach to pmi based word embeddings"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-4 (induced by source paper "all but the top simple and effective postprocessing for word representations"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "a latent variable model approach to pmi based word embeddings"
    - "gated feedback recurrent neural networks"
    - "from paraphrase database to compositional paraphrase model and back"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "wordnet2vec corpora agnostic word vectorization method"):
  with members:
    - "text understanding from scratch"
    - "distributed representations of sentences and documents"
    - "rehabilitation of count based models for word vector representations"
    - "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"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "learning to represent words in context with multilingual supervision"):
  with members:
    - "trans gram fast cross lingual word embeddings"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "end to end attention based large vocabulary speech recognition"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "adam a method for stochastic optimization"
    - "learning multilingual word representations using a bag of words autoencoder"
    - "embedding word similarity with neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "lexicon infused phrase embeddings for named entity resolution"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "factorial hidden markov models for learning representations of natural language"):
  with members:
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"
    - "frustratingly easy domain adaptation"
    - "domain adaptation for statistical classifiers"
    - "introduction to the conll 2000 shared task chunking"

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)