human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised object level video summarization with online motion auto encoder"):
  Abstract: Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos everyday. Despite the great progress achieved by prior works (e.g., the frame-level video summarization), the underlying fine-grained semantic and motion information (i.e., objects of interest and their key motions) in online videos has been barely touched, which is more essential and beneficial for many down-streaming tasks (e.g., object retrieval) in an intelligent system. In this paper, we investigate a pioneer research direction towards the fine-grained unsupervised object-level video summarization. It can be distinguished from existing pipelines in two aspects: extracting key motions of participated objects, and learning to summarize in an unsupervised and online manner that is more applicable for online growing videos. To achieve this goal, we propose a novel online motion Auto-Encoder (online motion-AE) framework that functions on the super-segmented object motion clips. The online motion-AE mimics the online dictionary learning for memorizing past states of object motions by continuously updating a tailored recurrent auto-encoder network. This online updating scheme enables the differentiable optimization of jointly online feature learning and dictionary learning to discriminate key object-motion clips. Finally, the key object-motion clips can be mined by using the reconstruction errors obtained by the online motion-AE. Comprehensive experiments on a newly-collected surveillance dataset and the public Base jumping, SumMe, and TVSum datasets have demonstrated the effectiveness of online motion-AE, and the application potential of fine-grained object-level video summarization.

Includes members:
  Member "dual motion gan for future flow embedded video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "interpretable structure evolving lstm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid")
  Member "learning to segment human by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few example object detection with model communication")
  Member "temporal dynamic graph lstm for action driven video object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "semantic object parsing with graph lstm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "summary transfer exemplar based subset selection for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "video storytelling textual summaries for events")
  Member "video summarization with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video storytelling textual summaries for events")
  Member "top down neural attention by excitation backprop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")

Question: Please tell me which class the source paper 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)