human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visual analysis of hidden state dynamics in recurrent neural networks"):
  Abstract: Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with domain specific structural annotations. We further show several use cases of the tool for analyzing specific hidden state properties on datasets containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.

Includes members:
  Member "predicting process behaviour using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decay replay mining to predict next process events")
  Member "towards better analysis of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "representation of linguistic form and function in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "visualizing and understanding neural models in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "rnn recurrent library for torch":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparing neural and n gram based language models for word segmentation")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "deep speech 2 end to end speech recognition in english and mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "visualizing and understanding recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")

Question: Please tell me which class the source paper 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)