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: "joint event detection and description in continuous video streams"
  Abstract: Dense video captioning is a fine-grained video understanding task that involves two sub-problems: localizing distinct events in a long video stream, and generating captions for the localized events. We propose the Joint Event Detection and Description Network (JEDDi-Net), which solves the dense video captioning task in an end-to-end fashion. Our model continuously encodes the input video stream with three-dimensional convolutional layers, proposes variable-length temporal events based on pooled features, and generates their captions. Proposal features are extracted within each proposal segment through 3D Segment-of-Interest pooling from shared video feature encoding. In order to explicitly model temporal relationships between visual events and their captions in a single video, we also propose a two-level hierarchical captioning module that keeps track of context. On the large-scale ActivityNet Captions dataset, JEDDi-Net demonstrates improved results as measured by standard metrics. We also present the first dense captioning results on the TACoS-MultiLevel dataset.

Connected through hyperedge HE-1 (induced by source paper "multi modal dense video captioning"):
  with members:
    - "watch listen and tell multi modal weakly supervised dense event captioning"
    - "memory attended recurrent network for video captioning"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "streamlined dense video captioning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "move forward and tell a progressive generator of video descriptions"

Connected through hyperedge HE-2 (induced by source paper "zero shot activity recognition with videos"):
  with members:
    - "human action recognition and prediction a survey"
    - "unseen action recognition with multimodal learning"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "zero shot activity recognition with verb attribute induction"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-3 (induced by source paper "human action sequence classification"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "streamlined dense video captioning"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "weakly supervised gaussian networks for action detection"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "timeception for complex action recognition"

Connected through hyperedge HE-4 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-5 (induced by source paper "watch listen and tell multi modal weakly supervised dense event captioning"):
  with members:
    - "an attempt towards interpretable audio visual video captioning"
    - "learning to localize sound source in visual scenes"
    - "indoor sound source localization with probabilistic neural network"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-6 (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"
    - "learning comment generation by leveraging user generated data"

Connected through hyperedge HE-7 (induced by source paper "multilevel language and vision integration for text to clip retrieval"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "modulating early visual processing by language"
    - "objects that sound"
    - "learning two branch neural networks for image text matching tasks"
    - "single shot temporal action detection"
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-8 (induced by source paper "unseen action recognition with multimodal learning"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "end to end dense video captioning with masked transformer"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning deep structure preserving image text embeddings"
    - "convolutional networks and learning invariant to homogeneous multiplicative scalings"
    - "deep visual semantic alignments for generating image descriptions"

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.CV(Computer Vision and Pattern Recognition)