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: "measuring praise and criticism inference of semantic orientation from association"
  Abstract: The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.

Connected through hyperedge HE-1 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"

Connected through hyperedge HE-4 (induced by source paper "leveraging structural and semantic correspondence for attribute oriented aspect sentiment discovery"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "improving topic models with latent feature word representations"
    - "learning disentangled representations of texts with application to biomedical abstracts"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "learning word ratings for empathy and distress from document level user responses"):
  with members:
    - "joint emotion label space modelling for affect lexica"
    - "modeling empathy and distress in reaction to news stories"
    - "aff2vec affect enriched distributional word representations"
    - "annotating and modeling empathy in spoken conversations"
    - "advances in pre training distributed word representations"
    - "a unified approach to interpreting model predictions"
    - "emotion intensities in tweets"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"

Connected through hyperedge HE-6 (induced by source paper "why so down the role of negative and positive pointwise mutual information in distributional semantics"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "swivel improving embeddings by noticing what s missing"
    - "bidirectional lstm crf models for sequence tagging"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "learning semantic sentence embeddings using sequential pair wise discriminator"):
  with members:
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "a survey on sentiment and emotion analysis for computational literary studies"):
  with members:
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "an analysis of emotion communication channels in fan fiction towards emotional storytelling"
    - "deep learning for sentiment analysis a survey"
    - "the evolution of sentiment analysis a review of research topics venues and top cited papers"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "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)