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: "seq2seq vis a visual debugging tool for sequence to sequence models"
  Abstract: Neural Sequence-to-Sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work in a five stage blackbox process that involves encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process. The aim is to identify which patterns have been learned and to detect model errors. We demonstrate the utility of our tool through several real-world large-scale sequence-to-sequence use cases.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting convolutional neural networks in nlp"):
  with members:
    - "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
    - "convolutional sequence to sequence learning"
    - "interpreting neural networks to improve politeness comprehension"
    - "representation of linguistic form and function in recurrent neural networks"
    - "a convolutional encoder model for neural machine translation"
    - "visualizing and understanding neural models in nlp"
    - "visualizing and understanding recurrent networks"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-2 (induced by source paper "a gray box interpretable visual debugging approach for deep sequence learning model"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "interactive classification for deep learning interpretation"
    - "direct manipulation visualization of deep networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "provenance for interactive visualizations"):
  with members:
    - "smoke fine grained lineage at interactive speed"
    - "algorithms for provisioning queries and analytics"
    - "timemachine timeline generation for knowledge base entities"
    - "rapid sampling for visualizations with ordering guarantees"

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)