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 supervised learning of depth and camera motion from 360 deg videos"):
  Abstract: As 360° cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360° perception becomes more and more important. We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360° video. In particular, starting from the SfMLearner, which is designed for cameras with normal field-of-view, we introduce three key features to process 360° images efficiently. Firstly, we convert each image from equirectangular projection to cubic projection in order to avoid image distortion. In each network layer, we use Cube Padding (CP), which pads intermediate features from adjacent faces, to avoid image boundaries. Secondly, we propose a novel "spherical" photometric consistency constraint on the whole viewing sphere. In this way, no pixel will be projected outside the image boundary which typically happens in images with normal field-of-view. Finally, rather than naively estimating six independent camera motions (i.e., naively applying SfM-Learner to each face on a cube), we propose a novel camera pose consistency loss to ensure the estimated camera motions reaching consensus. To train and evaluate our approach, we collect a new PanoSUNCG dataset containing a large amount of 360° videos with groundtruth depth and camera motion. Our approach achieves state-of-the-art depth prediction and camera motion estimation on PanoSUNCG with faster inference speed comparing to equirectangular. In real-world indoor videos, our approach can also achieve qualitatively reasonable depth prediction by acquiring model pre-trained on PanoSUNCG.

Includes members:
  Member "cube padding for weakly supervised saliency prediction in 360 deg videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "omnidirectional cnn for visual place recognition and navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "learning depth from monocular videos using direct methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "learning spherical convolution for fast features from 360 deg imagery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "deep 360 pilot learning a deep agent for piloting through 360 deg sports video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "360 o surface regression with a hyper sphere loss")

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)