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 "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  Abstract: Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view ($>180^\circ$). However, they come at the cost of strong non-linear distortion which require more complex algorithms. In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. Obtaining accurate and dense depth supervision is difficult in practice, but self-supervised learning approaches show promising results and could potentially overcome the problem. We present a novel self-supervised scale-aware framework for learning Euclidean distance and ego-motion from raw monocular fisheye videos without applying rectification. While it is possible to perform piece-wise linear approximation of fisheye projection surface and apply standard rectilinear models, it has its own set of issues like re-sampling distortion and discontinuities in transition regions. To encourage further research in this area, we will release this dataset as part of our WoodScape project \cite{yogamani2019woodscape}. We further evaluated the proposed algorithm on the KITTI dataset and obtained state-of-the-art results comparable to other self-supervised monocular methods. Qualitative results on an unseen fisheye video demonstrate impressive performance, see this https URL .

Includes members:
  Member "woodscape a multi task multi camera fisheye dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "3d packing for self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantically guided representation learning for self supervised monocular depth")
  Member "restricted deformable convolution based road scene semantic segmentation using surround view cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal semantic segmentation for fisheye urban driving images")
  Member "rethinking imagenet pre training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    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 "group normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "artistic style transfer for videos and spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a content transformation block for image style transfer")

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)