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: "modeling language vagueness in privacy policies using deep neural networks"
  Abstract: Website privacy policies are too long to read and difficult to understand. The over-sophisticated language makes privacy notices to be less effective than they should be. People become even less willing to share their personal information when they perceive the privacy policy as vague. This paper focuses on decoding vagueness from a natural language processing perspective. While thoroughly identifying the vague terms and their linguistic scope remains an elusive challenge, in this work we seek to learn vector representations of words in privacy policies using deep neural networks. The vector representations are fed to an interactive visualization tool (LSTMVis) to test on their ability to discover syntactically and semantically related vague terms. The approach holds promise for modeling and understanding language vagueness.

Connected through hyperedge HE-1 (induced by source paper "automatic detection of vague words and sentences in privacy policies"):
  with members:
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "adversarial learning for neural dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "nips 2016 tutorial generative adversarial networks"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "named entity recognition with bidirectional lstm cnns"
    - "conditional generative adversarial nets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "polisis automated analysis and presentation of privacy policies using deep learning"):
  with members:
    - "enriching word vectors with subword information"
    - "applying deep learning to answer selection a study and an open task"
    - "visual instance retrieval with deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"):
  with members:
    - "predicting process behaviour using deep learning"
    - "towards better analysis of deep convolutional neural networks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "visualizing and understanding neural models in nlp"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "visualizing and understanding recurrent networks"
    - "semi supervised sequence learning"

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)