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: "automatic article commenting the task and dataset"
  Abstract: Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments' varying quality. Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.

Connected through hyperedge HE-1 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"

Connected through hyperedge HE-2 (induced by source paper "read attend and comment a deep architecture for automatic news comment generation"):
  with members:
    - "challenges for toxic comment classification an in depth error analysis"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "know what you don t know unanswerable questions for squad"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "coherent comment generation for chinese articles with a graph to sequence model"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "sql to text generation with graph to sequence model"
    - "graph convolutional neural networks for web scale recommender systems"
    - "multiresolution graph attention networks for relevance matching"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "diversified co attention towards informative live video commenting"):
  with members:
    - "automatic generation of personalized comment based on user profile"
    - "deep modular co attention networks for visual question answering"
    - "livebot generating live video comments based on visual and textual contexts"
    - "move forward and tell a progressive generator of video descriptions"
    - "multi pointer co attention networks for recommendation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "joint event detection and description in continuous video streams"
    - "learning comment generation by leveraging user generated data"

Connected through hyperedge HE-5 (induced by source paper "automatic generation of personalized comment based on user profile"):
  with members:
    - "livebot generating live video comments based on visual and textual contexts"
    - "learning comment generation by leveraging user generated data"
    - "unsupervised machine commenting with neural variational topic model"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "affect lm a neural language model for customizable affective text generation"
    - "a persona based neural conversation model"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "unsupervised machine commenting with neural variational topic model"):
  with members:
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "sengen sentence generating neural variational topic model"
    - "clustering is efficient for approximate maximum inner product search"
    - "neural variational inference for text processing"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "quantization based fast inner product search"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "chinese pinyin aided ime input what you have not keystroked yet"):
  with members:
    - "finding better subword segmentation for neural machine translation"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "neural machine translation with extended context"
    - "tracing a loose wordhood for chinese input method engine"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

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)