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: "document embedding with paragraph vectors"
  Abstract: Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts. In their work, the authors showed that the method can learn an embedding of movie review texts which can be leveraged for sentiment analysis. That proof of concept, while encouraging, was rather narrow. Here we consider tasks other than sentiment analysis, provide a more thorough comparison of Paragraph Vectors to other document modelling algorithms such as Latent Dirichlet Allocation, and evaluate performance of the method as we vary the dimensionality of the learned representation. We benchmarked the models on two document similarity data sets, one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method performs significantly better than other methods, and propose a simple improvement to enhance embedding quality. Somewhat surprisingly, we also show that much like word embeddings, vector operations on Paragraph Vectors can perform useful semantic results.

Connected through hyperedge HE-1 (induced by source paper "automatic location type classification from social media posts"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a neural model for user geolocation and lexical dialectology"
    - "neural structural correspondence learning for domain adaptation"
    - "character level convolutional networks for text classification"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "named entity recognition with bidirectional lstm cnns"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "a scalable chatbot platform leveraging online community posts a proof of concept study"):
  with members:
    - "topic aware neural response generation"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "neural responding machine for short text conversation"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "encouraging paragraph embeddings to remember sentence identity improves classification"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "data augmentation for low resource sentiment analysis using generative adversarial networks"):
  with members:
    - "on enhancing speech emotion recognition using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "towards open set identity preserving face synthesis"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "semi supervised and transfer learning approaches for low resource sentiment classification"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "exprgan facial expression editing with controllable expression intensity"

Connected through hyperedge HE-6 (induced by source paper "fast intent classification for spoken language understanding"):
  with members:
    - "one vs all models for asynchronous training an empirical analysis"
    - "statistical model compression for small footprint natural language understanding"
    - "a re ranker scheme for integrating large scale nlu models"
    - "spoken language understanding on the edge"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-7 (induced by source paper "hierarchical document encoder for parallel corpus mining"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "word mover s embedding from word2vec to document embedding"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "efficient vector representation for documents through corruption"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "race ethnicity and national origin based discrimination in social media and hate crimes across 100 u s cities"):
  with members:
    - "the effect of extremist violence on hateful speech online"
    - "socio spatial self organizing maps using social media to assess relevant geographies for exposure to social processes"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "ex machina personal attacks seen at scale"

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)