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: "livebot generating live video comments based on visual and textual contexts"
  Abstract: We introduce the task of automatic live commenting. Live commenting, which is also called `video barrage', is an emerging feature on online video sites that allows real-time comments from viewers to fly across the screen like bullets or roll at the right side of the screen. The live comments are a mixture of opinions for the video and the chit chats with other comments. Automatic live commenting requires AI agents to comprehend the videos and interact with human viewers who also make the comments, so it is a good testbed of an AI agent's ability of dealing with both dynamic vision and language. In this work, we construct a large-scale live comment dataset with 2,361 videos and 895,929 live comments. Then, we introduce two neural models to generate live comments based on the visual and textual contexts, which achieve better performance than previous neural baselines such as the sequence-to-sequence model. Finally, we provide a retrieval-based evaluation protocol for automatic live commenting where the model is asked to sort a set of candidate comments based on the log-likelihood score, and evaluated on metrics such as mean-reciprocal-rank. Putting it all together, we demonstrate the first `LiveBot'.

Connected through hyperedge HE-1 (induced by source paper "multimodal matching transformer for live commenting"):
  with members:
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "video description a survey of methods datasets and evaluation metrics"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "video captioning via hierarchical reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "see hear and read deep aligned representations"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning comment generation by leveraging user generated data"

Connected through hyperedge HE-3 (induced by source paper "a dual reinforcement learning framework for unsupervised text style transfer"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "sgm sequence generation model for multi label classification"
    - "style transfer through back translation"
    - "multi reward reinforced summarization with saliency and entailment"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-4 (induced by source paper "aligning visual regions and textual concepts learning fine grained image representations for image captioning"):
  with members:
    - "exploring and distilling cross modal information for image captioning"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-5 (induced by source paper "automatic generation of personalized comment based on user profile"):
  with members:
    - "automatic article commenting the task and dataset"
    - "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"

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)