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: "depechemood a lexicon for emotion analysis from crowd annotated news"
  Abstract: While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na\"{\i}ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

Connected through hyperedge HE-1 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-2 (induced by source paper "emotionally aware chatbots a survey"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "sounding board a user centric and content driven social chatbot"
    - "touch your heart a tone aware chatbot for customer care on social media"

Connected through hyperedge HE-3 (induced by source paper "affect in tweets using experts model"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "emotion intensities in tweets"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "emo2vec learning generalized emotion representation by multi task training"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "tensor fusion network for multimodal sentiment analysis"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "learning language representations for typology prediction"
    - "deep multitask learning for semantic dependency parsing"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-5 (induced by source paper "emotion detection in text a review"):
  with members:
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "counter fitting word vectors to linguistic constraints"
    - "an ensemble method to produce high quality word embeddings"
    - "rule based emotion detection on social media putting tweets on plutchik s wheel"
    - "retrofitting word vectors to semantic lexicons"
    - "sentiment analysis in the news"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"):
  with members:
    - "word affect intensities"
    - "wassa 2017 shared task on emotion intensity"
    - "analysis of italian word embeddings"
    - "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-7 (induced by source paper "depression and self harm risk assessment in online forums"):
  with members:
    - "a short review of ethical challenges in clinical natural language processing"
    - "natural language processing for mental health large scale discourse analysis of counseling conversations"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "skip thought vectors"
    - "adam a method for stochastic optimization"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-8 (induced by source paper "fortia fbk at semeval 2017 task 5 bullish or bearish inferring sentiment towards brands from financial news headlines"):
  with members:
    - "word embeddings and their use in sentence classification tasks"
    - "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "natural language processing almost from scratch"

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)