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 "decision support with text based emotion recognition deep learning for affective computing"):
  Abstract: Emotions widely affect the decision-making of humans and, hence, affective computing takes emotional states into account with the goal of tailoring decision support to individuals. However, the accurate recognition of emotions within narrative materials presents a challenging undertaking due to the complexity and ambiguity of language. Even though deep learning has evolved as the state-of-the-art in various tasks from text mining, its benefits with regard to affective computing are not yet understood. We thus propose the following innovations: (1) we adapt recurrent neural networks from the field of deep learning to affective computing. (2) We extend these networks for predicting the score of different affective dimensions. (3) We implement transfer learning for pre-training word embeddings. Analyzing the results, we find that deep learning consistently outperforms traditional machine learning with improvements of up to 21% in F1-score when labeling emotions and 6% in forecast errors when rating the intensity of emotions. Altogether, the findings have considerable implications for the use of affective computing in providing decision support.

Includes members:
  Member "putting question answering systems into practice transfer learning for efficient domain customization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications")
  Member "deep learning in business analytics and operations research models applications and managerial implications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization")
  Member "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a combined cnn and lstm model for arabic sentiment analysis")
  Member "predicting process behaviour using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decay replay mining to predict next process events")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "how transferable are neural networks in nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal embeddings and transformer models for narrative text understanding")
  Member "detecting state of aggression in sentences using cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for affective computing text based emotion recognition in decision support")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text classification with lexicon from preattention mechanism")

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.CL(Computation and Language)