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: "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
  Abstract: Abstract   Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree. The underlying tensors infer the salient passages of narrative materials. In addition, we suggest two algorithms for data augmentation (node reordering and artificial leaf insertion) that increase our training set and reduce overfitting. Our benchmarks demonstrate the superior performance of our approach. Moreover, our tensor structure reveals the salient text passages and thereby provides explanatory insights.

Connected through hyperedge HE-1 (induced by source paper "knowledge guided unsupervised rhetorical parsing for text summarization"):
  with members:
    - "global encoding for abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "adapting the neural encoder decoder framework from single to multi document summarization"
    - "neural document summarization by jointly learning to score and select sentences"
    - "unsupervised machine translation using monolingual corpora only"
    - "get to the point summarization with pointer generator networks"
    - "neural discourse structure for text categorization"
    - "neural summarization by extracting sentences and words"

Connected through hyperedge HE-2 (induced by source paper "a joint text mining rank size investigation of the rhetoric structures of the us presidents speeches"):
  with members:
    - "punctuation effects in english and esperanto texts"
    - "zipf s law and the creation of musical context"

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"
    - "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"
    - "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)