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: "learning comment generation by leveraging user generated data"
  Abstract: Existing models on open-domain comment generation produce repetitive and uninteresting response. To cope with this issue, we propose a combined approach of retrieval and generation methods. We introduce an attentive scorer to retrieve informative and relevant comments by using user-generated data. Then, we use the retrieved comments to train our sequence-to-sequence model with copy mechanism to copy important keywords from articles. We show the robustness of our model, and it can alleviate the issue. In our experiments, our proposed generative model significantly outperforms the Seq2Seq with attention model and Information Retrieval models by around 27 and 30 BLEU-1 points respectively.

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 "personalizing dialogue agents via meta learning"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "global to local memory pointer networks for task oriented dialogue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "training millions of personalized dialogue agents"
    - "dialogue natural language inference"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "personalizing dialogue agents i have a dog do you have pets too"

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"
    - "automatic article commenting the task and dataset"
    - "joint event detection and description in continuous video streams"

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"
    - "automatic article commenting the task and dataset"
    - "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 "code switched language models using neural based synthetic data from parallel sentences"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "code switching language modeling using syntax aware multi task learning"
    - "get to the point summarization with pointer generator networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"
    - "syntactic and semantic features for code switching factored language models"

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)