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 summarization using fully convolutional sequence networks"
  Abstract: This paper addresses the problem of video summarization. Given an input video, the goal is to select a subset of the frames to create a summary video that optimally captures the important information of the input video. With the large amount of videos available online, video summarization provides a useful tool that assists video search, retrieval, browsing, etc. In this paper, we formulate video summarization as a sequence labeling problem. Unlike existing approaches that use recurrent models, we propose fully convolutional sequence models to solve video summarization. We firstly establish a novel connection between semantic segmentation and video summarization, and then adapt popular semantic segmentation networks for video summarization. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of our models.

Connected through hyperedge HE-1 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation"):
  with members:
    - "cycle sum cycle consistent adversarial lstm networks for unsupervised video summarization"
    - "video summarization via actionness ranking"
    - "discriminative feature learning for unsupervised video summarization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "aenet learning deep audio features for video analysis"
    - "video summarization with long short term memory"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "convolutional hierarchical attention network for query focused video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "query conditioned three player adversarial network for video summarization"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-3 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "susinet see understand and summarize it"):
  with members:
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "taskonomy disentangling task transfer learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "multimodal visual concept learning with weakly supervised techniques"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "dealing with ambiguity in robotic grasping via multiple predictions"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "towards accurate multi person pose estimation in the wild"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "convolutional pose machines"
    - "robotic grasp detection using deep convolutional neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-6 (induced by source paper "video summarization by learning from unpaired data"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "query conditioned three player adversarial network for video summarization"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-7 (induced by source paper "learning video summarization using unpaired data"):
  with members:
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"
    - "energy based generative adversarial network"
    - "video summarization with long short term memory"
    - "adam a method for stochastic optimization"

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)