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: "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.

Connected through hyperedge HE-1 (induced by source paper "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "emotionlines an emotion corpus of multi party conversations"
    - "memory fusion network for multi view sequential learning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "neural message passing for quantum chemistry"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "emotion recognition in conversation research challenges datasets and recent advances"):
  with members:
    - "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition for vietnamese social media text"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "deep learning versus traditional classifiers on vietnamese students feedback corpus"
    - "iest wassa 2018 implicit emotions shared task"
    - "a factoid question answering system for vietnamese"
    - "from word segmentation to pos tagging for vietnamese"
    - "emotion intensities in tweets"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"
    - "similarity encoding for learning with dirty categorical variables"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "deep embedding forest forest based serving with deep embedding features"
    - "accelerating innovation through analogy mining"

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)