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: "learning word meta embeddings by using ensembles of embedding sets"
  Abstract: Word embeddings -- distributed representations of words -- in deep learning are beneficial for many tasks in natural language processing (NLP). However, different embedding sets vary greatly in quality and characteristics of the captured semantics. Instead of relying on a more advanced algorithm for embedding learning, this paper proposes an ensemble approach of combining different public embedding sets with the aim of learning meta-embeddings. Experiments on word similarity and analogy tasks and on part-of-speech tagging show better performance of meta-embeddings compared to individual embedding sets. One advantage of meta-embeddings is the increased vocabulary coverage. We will release our meta-embeddings publicly.

Connected through hyperedge HE-1 (induced by source paper "detecting cybersecurity events from noisy short text"):
  with members:
    - "learning domain specific word embeddings from sparse cybersecurity texts"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "vqa visual question answering"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "microsoft ai challenge india 2018 learning to rank passages for web question answering with deep attention networks"):
  with members:
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enriching word vectors with subword information"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic coattention networks for question answering"
    - "pointer sentinel mixture models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "med2meta learning representations of medical concepts with meta embeddings"):
  with members:
    - "mixed pooling multi view attention autoencoder for representation learning in healthcare"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "think globally embed locally locally linear meta embedding of words"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "gram graph based attention model for healthcare representation learning"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "angular based word meta embedding learning"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "a latent variable model approach to pmi based word embeddings"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "addressing the rare word problem in neural machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "compositional approaches for representing relations between words a comparative study"):
  with members:
    - "issues in evaluating semantic spaces using word analogies"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "holographic embeddings of knowledge graphs"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "measuring semantic similarity by latent relational analysis"

Connected through hyperedge HE-8 (induced by source paper "think globally embed locally locally linear meta embedding of words"):
  with members:
    - "on the convergent properties of word embedding methods"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "embedding word similarity with neural machine translation"
    - "not all neural embeddings are born equal"
    - "the expressive power of word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)