human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "affect in tweets using experts model"):
  Abstract: Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.

Includes members:
  Member "learning to generate reviews and discovering sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "seernet at semeval 2018 task 1 domain adaptation for affect in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "wassa 2017 shared task on emotion intensity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "seernet at emoint 2017 tweet emotion intensity estimator":
    also connected through other hyperedges:
      - 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")
  Member "emotion intensities in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "depechemood a lexicon for emotion analysis from crowd annotated news":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)