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: "deep learning for affective computing text based emotion recognition in decision support"
  Abstract: Abstract   Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.

Connected through hyperedge HE-1 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "adaptive document retrieval for deep question answering"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "reading wikipedia to answer open domain questions"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "learning structured text representations"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "neural discourse structure for text categorization"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "better document level sentiment analysis from rst discourse parsing"
    - "improved semantic representations from tree structured long short term memory networks"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-3 (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"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "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)