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 memory network approach for story based temporal summarization of 360 deg videos"):
  Abstract: We address the problem of story-based temporal summarization of long 360{\deg} videos. We propose a novel memory network model named Past-Future Memory Network (PFMN), in which we first compute the scores of 81 normal field of view (NFOV) region proposals cropped from the input 360{\deg} video, and then recover a latent, collective summary using the network with two external memories that store the embeddings of previously selected subshots and future candidate subshots. Our major contributions are two-fold. First, our work is the first to address story-based temporal summarization of 360{\deg} videos. Second, our model is the first attempt to leverage memory networks for video summarization tasks. For evaluation, we perform three sets of experiments. First, we investigate the view selection capability of our model on the Pano2Vid dataset. Second, we evaluate the temporal summarization with a newly collected 360{\deg} video dataset. Finally, we experiment our model's performance in another domain, with image-based storytelling VIST dataset. We verify that our model achieves state-of-the-art performance on all the tasks.

Includes members:
  Member "a deep ranking model for spatio temporal highlight detection from a 360 video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images")
  Member "deep 360 pilot learning a deep agent for piloting through 360 deg sports video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "360 o surface regression with a hyper sphere loss")
  Member "a read write memory network for movie story understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "hierarchically attentive rnn for album summarization and storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")
  Member "making 360 circ video watchable in 2d learning videography for click free viewing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images")
  Member "learning visual storylines with skipping recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating persuasive visual storylines for promotional videos")
  Member "summary transfer exemplar based subset selection for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video storytelling textual summaries for events")
  Member "dynamic neural turing machine with soft and hard addressing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to follow directions in street view")

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)