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 "self view grounding given a narrated 360 deg video"):
  Abstract: Narrated 360{\deg} videos are typically provided in many touring scenarios to mimic real-world experience. However, previous work has shown that smart assistance (i.e., providing visual guidance) can significantly help users to follow the Normal Field of View (NFoV) corresponding to the narrative. In this project, we aim at automatically grounding the NFoVs of a 360{\deg} video given subtitles of the narrative (referred to as "NFoV-grounding"). We propose a novel Visual Grounding Model (VGM) to implicitly and efficiently predict the NFoVs given the video content and subtitles. Specifically, at each frame, we efficiently encode the panorama into feature map of candidate NFoVs using a Convolutional Neural Network (CNN) and the subtitles to the same hidden space using an RNN with Gated Recurrent Units (GRU). Then, we apply soft-attention on candidate NFoVs to trigger sentence decoder aiming to minimize the reconstruct loss between the generated and given sentence. Finally, we obtain the NFoV as the candidate NFoV with the maximum attention without any human supervision. To train VGM more robustly, we also generate a reverse sentence conditioning on one minus the soft-attention such that the attention focuses on candidate NFoVs less relevant to the given sentence. The negative log reconstruction loss of the reverse sentence (referred to as "irrelevant loss") is jointly minimized to encourage the reverse sentence to be different from the given sentence. To evaluate our method, we collect the first narrated 360{\deg} videos dataset and achieve state-of-the-art NFoV-grounding performance.

Includes members:
  Member "saltinet scan path prediction on 360 degree images using saliency volumes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "viewport aware deep reinforcement learning approach for 360 o video caching")
  Member "discriminative bimodal networks for visual localization and detection with natural language queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "deep 360 pilot learning a deep agent for piloting through 360 deg sports video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "360 o surface regression with a hyper sphere loss")
  Member "weakly supervised visual grounding of phrases with linguistic structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")
  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 "grounding of textual phrases in images by reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "leveraging video descriptions to learn video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual question answering on 360 deg images")
  Member "title generation for user generated videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research")

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)