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: "the expressive power of word embeddings"
  Abstract: We seek to better understand the information encoded in word embeddings. We propose several tasks that help to distinguish the characteristics of different publicly released embeddings. Our evaluation shows that embeddings are able to capture surprisingly nuanced semantics even in the absence of sentence structure. Moreover, benchmarking the embeddings shows great variance in quality and characteristics of the semantics captured by the tested embeddings. Finally, we show the impact of varying the number of dimensions and the resolution of each dimension on the effective useful features captured by the embedding space. Our contributions highlight the importance of embeddings for NLP tasks and the effect of their quality on the final results.

Connected through hyperedge HE-1 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-2 (induced by source paper "examining structure of word embeddings with pca"):
  with members:
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a survey of word embeddings evaluation methods"
    - "attention is all you need"
    - "what do neural machine translation models learn about morphology"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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"
    - "learning word meta embeddings by using ensembles of embedding sets"

Connected through hyperedge HE-4 (induced by source paper "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"):
  with members:
    - "think globally embed locally locally linear meta embedding of words"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "neural language modeling by jointly learning syntax and lexicon"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "tree structured neural machine for linguistics aware sentence generation"
    - "long short term memory networks for machine reading"
    - "recurrent neural network grammars"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent highway networks"
    - "a fast unified model for parsing and sentence understanding"

Connected through hyperedge HE-6 (induced by source paper "think globally embed locally locally linear meta embedding of words"):
  with members:
    - "on the convergent properties of word embedding methods"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "embedding word similarity with neural machine translation"
    - "not all neural embeddings are born equal"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "synonym discovery with etymology based word embeddings"):
  with members:
    - "optimal cur matrix decompositions"
    - "efficient estimation of word representations in vector space"
    - "the numpy array a structure for efficient numerical computation"
    - "a network of two chinese character compound words in the japanese language"

Connected through hyperedge HE-8 (induced by source paper "multichannel variable size convolution for sentence classification"):
  with members:
    - "deep learning for answer sentence selection"
    - "distributed representations of sentences and documents"
    - "not all neural embeddings are born equal"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)