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: "neural wikipedian generating textual summaries from knowledge base triples"
  Abstract: Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.

Connected through hyperedge HE-1 (induced by source paper "triple to text converting rdf triples into high quality natural languages via optimizing an inverse kl divergence"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "cot cooperative training for generative modeling of discrete data"
    - "texygen a benchmarking platform for text generation models"
    - "adversarially regularized autoencoders"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "logician a unified end to end neural approach for open domain information extraction"):
  with members:
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "question answering and question generation as dual tasks"
    - "answering complex questions using open information extraction"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-3 (induced by source paper "entity summarization state of the art and future challenges"):
  with members:
    - "eventkg the hub of event knowledge on the web and biographical timeline generation"
    - "learning to generate one sentence biographies from wikidata"
    - "neural text generation from structured data with application to the biography domain"
    - "graph summarization methods and applications a survey"
    - "timemachine timeline generation for knowledge base entities"
    - "leveraging usage data for linked data movie entity summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "generating titles for web tables"):
  with members:
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "learning to generate one sentence biographies from wikidata"
    - "learning to skim text"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-5 (induced by source paper "learning to generate wikipedia summaries for underserved languages from wikidata"):
  with members:
    - "learning to ask neural question generation for reading comprehension"
    - "learning to generate one sentence biographies from wikidata"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "noisy activation functions"
    - "neural text generation from structured data with application to the biography domain"
    - "a neural attention model for abstractive sentence summarization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "addressing the rare word problem in neural machine translation"

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)