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 "a graph based framework to bridge movies and synopses"):
  Abstract: Inspired by the remarkable advances in video analytics, research teams are stepping towards a greater ambition -- movie understanding. However, compared to those activity videos in conventional datasets, movies are significantly different. Generally, movies are much longer and consist of much richer temporal structures. More importantly, the interactions among characters play a central role in expressing the underlying story. To facilitate the efforts along this direction, we construct a dataset called Movie Synopses Associations (MSA) over 327 movies, which provides a synopsis for each movie, together with annotated associations between synopsis paragraphs and movie segments. On top of this dataset, we develop a framework to perform matching between movie segments and synopsis paragraphs. This framework integrates different aspects of a movie, including event dynamics and character interactions, and allows them to be matched with parsed paragraphs, based on a graph-based formulation. Our study shows that the proposed framework remarkably improves the matching accuracy over conventional feature-based methods. It also reveals the importance of narrative structures and character interactions in movie understanding.

Includes members:
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "person search in videos with one portrait through visual and temporal links":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wider face and pedestrian challenge 2018 methods and results")
  Member "a joint sequence fusion model for video question answering and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "from trailers to storylines an efficient way to learn from movies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are we asking the right questions in movieqa")
  Member "multilevel language and vision integration for text to clip retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "unifying identification and context learning for person recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning context graph for person search")
  Member "holistic multi modal memory network for movie question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "dual encoding for zero example video retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")

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)