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: "wassa 2017 shared task on emotion intensity"
  Abstract: We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best--worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

Connected through hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "an overview of multi task learning in deep neural networks"
    - "emotion intensities in tweets"

Connected through hyperedge HE-2 (induced by source paper "establishing human robot trust through music driven robotic emotion prosody and gesture"):
  with members:
    - "a unit selection methodology for music generation using deep neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "gwu nlp lab at semeval 2019 task 3 emocontext effective contextual information in models for emotion detection in sentence level in a multigenre corpus"):
  with members:
    - "deep contextualized word representations"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "advances in pre training distributed word representations"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "a sentiment and semantics based approach for emotion detection in textual conversations"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "a lexical syntactic and semantic perspective for understanding style in text"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"
    - "a multi task ensemble framework for emotion sentiment and intensity prediction"
    - "style transfer from non parallel text by cross alignment"
    - "style transfer in text exploration and evaluation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"

Connected through hyperedge HE-6 (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"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "emotion intensities in tweets"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "exploring fine tuned embeddings that model intensifiers for emotion analysis"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical analysis of the role of amplifiers downtoners and negations in emotion classification in microblogs"
    - "sentiment composition of words with opposing polarities"
    - "the effect of negators modals and degree adverbs on sentiment composition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "an analysis of emotion communication channels in fan fiction towards emotional storytelling"):
  with members:
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "controllable neural story plot generation via reinforcement learning"
    - "mojitalk generating emotional responses at scale"
    - "the emotional arcs of stories are dominated by six basic shapes"

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)