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: "non distributional word vector representations"
  Abstract: Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We analyze their performance on state-of-the-art evaluation methods for distributional models of word vectors and find they are competitive to standard distributional approaches.

Connected through hyperedge HE-1 (induced by source paper "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "consistent dialogue generation with self supervised feature learning"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "investigating capsule networks with dynamic routing for text classification"
    - "attention is all you need"
    - "sparse representation based open set recognition"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-2 (induced by source paper "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"):
  with members:
    - "cross lingual models of word embeddings an empirical comparison"
    - "a network based end to end trainable task oriented dialogue system"
    - "a dual embedding space model for document ranking"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "charagram embedding words and sentences via character n grams"
    - "neural belief tracker data driven dialogue state tracking"
    - "many languages one parser"

Connected through hyperedge HE-3 (induced by source paper "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"):
  with members:
    - "expanding subjective lexicons for social media mining with embedding subspaces"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "creating causal embeddings for question answering with minimal supervision"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"
    - "wordrep a benchmark for research on learning word representations"

Connected through hyperedge HE-4 (induced by source paper "calculating semantic similarity between academic articles using topic event and ontology"):
  with members:
    - "distributed representations of sentences and documents"
    - "an improved semantic similarity measure for document clustering based on topic maps"
    - "efficient estimation of word representations in vector space"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-5 (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "baselines and test data for cross lingual inference"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a decomposable attention model for natural language inference"
    - "intrinsic subspace evaluation of word embedding representations"
    - "counter fitting word vectors to linguistic constraints"

Connected through hyperedge HE-6 (induced by source paper "charagram embedding words and sentences via character n grams"):
  with members:
    - "character based neural machine translation"
    - "counter fitting word vectors to linguistic constraints"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "learning distributed representations of sentences from unlabelled data"
    - "exploring the limits of language modeling"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "simverb 3500 a large scale evaluation set of verb similarity"):
  with members:
    - "the role of context types and dimensionality in learning word embeddings"
    - "counter fitting word vectors to linguistic constraints"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "from paraphrase database to compositional paraphrase model and back"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "word representations via gaussian embedding"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-8 (induced by source paper "detecting sarcasm in multimodal social platforms"):
  with members:
    - "automatic sarcasm detection a survey"
    - "yfcc100m the new data in multimedia research"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "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"
    - "caffe convolutional architecture for fast feature embedding"

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)