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: "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
  Abstract: With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.

Connected through hyperedge HE-1 (induced by source paper "interactive variance attention based online spoiler detection for time sync comments"):
  with members:
    - "time sync video tag extraction using semantic association graph"
    - "legal judgment prediction via multi perspective bi feedback network"
    - "herding effect based attention for personalized time sync video recommendation"
    - "fine grained video attractiveness prediction using multimodal deep learning on a large real world dataset"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "end to end memory networks"

Connected through hyperedge HE-2 (induced by source paper "towards extracting highlights from recorded live videos an implicit crowdsourcing approach"):
  with members:
    - "deeplens towards a visual data management system"
    - "video highlight prediction using audience chat reactions"
    - "real time video highlights for yahoo esports"
    - "video summarization using deep semantic features"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "generating sequences with recurrent neural networks"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-3 (induced by source paper "smartbullets a cloud assisted bullet screen filter based on deep learning"):
  with members:
    - "convolutional sequence to sequence learning"
    - "bidirectional lstm crf models for sequence tagging"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "game based video context dialogue"):
  with members:
    - "chalet cornell house agent learning environment"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "video highlight prediction using audience chat reactions"
    - "a structured self attentive sentence embedding"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "a persona based neural conversation model"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)