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: "the effect of negators modals and degree adverbs on sentiment composition"
  Abstract: Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.

Connected through hyperedge HE-1 (induced by source paper "sentiment analysis is not solved assessing and probing sentiment classification"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reducing gender bias in abusive language detection"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-2 (induced by source paper "a fine grained sentiment dataset for norwegian"):
  with members:
    - "progressive self supervised attention learning for aspect level sentiment analysis"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "norec the norwegian review corpus"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "uncovering flaming events on news media in social media"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "inspecting interactions online news media synergies in social media"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "netspam a network based spam detection framework for reviews in online social media"
    - "fasttext zip compressing text classification models"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "wassa 2017 shared task on emotion intensity"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "an affect rich neural conversational model with biased attention and weighted cross entropy loss"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "affect lm a neural language model for customizable affective text generation"
    - "affective neural response generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-6 (induced by source paper "sentiment composition of words with opposing polarities"):
  with members:
    - "capturing reliable fine grained sentiment associations by crowdsourcing and best worst scaling"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a statistical parsing framework for sentiment classification"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-7 (induced by source paper "an empirical analysis of the role of amplifiers downtoners and negations in emotion classification in microblogs"):
  with members:
    - "iest wassa 2018 implicit emotions shared task"
    - "wassa 2017 shared task on emotion intensity"
    - "crowdsourcing a word emotion association lexicon"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"):
  with members:
    - "crowdsourcing a word emotion association lexicon"

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)