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: "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
  Abstract: Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which "summarizes" the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further "summarization" of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.

Connected through hyperedge HE-1 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"):
  with members:
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "structured attention networks"
    - "learning structured text representations"
    - "get to the point summarization with pointer generator networks"
    - "neural discourse structure for text categorization"
    - "a deep reinforced model for abstractive summarization"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-2 (induced by source paper "exploring hierarchical interaction between review and summary for better sentiment analysis"):
  with members:
    - "attention is not explanation"
    - "interpretable neural predictions with differentiable binary variables"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "rationale augmented convolutional neural networks for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  with members:
    - "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "softtarget regularization an effective technique to reduce over fitting in neural networks"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

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)