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 deep semantic features"
  Abstract: This paper presents a video summarization technique for an Internet video to provide a quick way to overview its content. This is a challenging problem because finding important or informative parts of the original video requires to understand its content. Furthermore the content of Internet videos is very diverse, ranging from home videos to documentaries, which makes video summarization much more tough as prior knowledge is almost not available. To tackle this problem, we propose to use deep video features that can encode various levels of content semantics, including objects, actions, and scenes, improving the efficiency of standard video summarization techniques. For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions. To generate a video summary, we extract the deep features from each segment of the original video and apply a clustering-based summarization technique to them. We evaluate our video summaries using the SumMe dataset as well as baseline approaches. The results demonstrated the advantages of incorporating our deep semantic features in a video summarization technique.

Connected through hyperedge HE-1 (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"
    - "video summarization using fully convolutional sequence networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"

Connected through hyperedge HE-3 (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"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "real time video highlights for yahoo esports"
    - "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-4 (induced by source paper "rethinking the evaluation of video summaries"):
  with members:
    - "query adaptive video summarization via quality aware relevance estimation"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "real time video highlights for yahoo esports"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "videoset video summary evaluation through text"

Connected through hyperedge HE-5 (induced by source paper "summarizing videos with attention"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "amnet memorability estimation with attention"
    - "learning video summarization using unpaired data"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "attention is all you need"
    - "video summarization with attention based encoder decoder networks"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "summarization of user generated sports video by using deep action recognition features"):
  with members:
    - "convolutional pose machines"
    - "video summarization with long short term memory"
    - "spatiotemporal residual networks for video action recognition"
    - "title generation for user generated videos"
    - "real time human pose estimation from video with convolutional neural networks"
    - "beyond short snippets deep networks for video classification"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "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)