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: "an empirical evaluation of doc2vec with practical insights into document embedding generation"
  Abstract: Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec (Mikolov et al., 2013a) to learn document-level embeddings. Despite promising results in the original paper, others have struggled to reproduce those results. This paper presents a rigorous empirical evaluation of doc2vec over two tasks. We compare doc2vec to two baselines and two state-of-the-art document embedding methodologies. We found that doc2vec performs robustly when using models trained on large external corpora, and can be further improved by using pre-trained word embeddings. We also provide recommendations on hyper-parameter settings for general purpose applications, and release source code to induce document embeddings using our trained doc2vec models.

Connected through hyperedge HE-1 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "knowledge based word sense disambiguation using topic models"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "distributional measures of semantic distance a survey"

Connected through hyperedge HE-3 (induced by source paper "hybrid deep embedding for recommendations with dynamic aspect level explanations"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "self attentive sequential recommendation"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "heteroglossia in situ story ideation with the crowd"):
  with members:
    - "mechanical novel crowdsourcing complex work through reflection and revision"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "vector representations of text data in deep learning"):
  with members:
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "starspace embed all the things"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "swivel improving embeddings by noticing what s missing"
    - "node2vec scalable feature learning for networks"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-6 (induced by source paper "my way of telling a story persona based grounded story generation"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "glac net glocal attention cascading networks for multi image cued story generation"

Connected through hyperedge HE-7 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-8 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

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)