human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"
  Abstract: Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction.

Connected through hyperedge HE-1 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression"):
  with members:
    - "lf net learning local features from images"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "improved visual relocalization by discovering anchor points"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-2 (induced by source paper "large scale real time visual inertial localization revisited"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "identifying robust landmarks in feature based maps"
    - "hybrid scene compression for visual localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-3 (induced by source paper "surfelmeshing online surfel based mesh reconstruction"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "real time cpu based large scale 3d mesh reconstruction"
    - "efficient online surface correction for real time large scale 3d reconstruction"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "efficient moving point handling for incremental 3d manifold reconstruction"

Connected through hyperedge HE-4 (induced by source paper "where should we place lidars on the autonomous vehicle an optimal design approach"):
  with members:
    - "an optimal lidar configuration approach for self driving cars"
    - "autonomous driving without a burden view from outside with elevated lidar"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of depth and camera motion from 360 deg videos"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"

Connected through hyperedge HE-6 (induced by source paper "efficient 2d 3d matching for multi camera visual localization"):
  with members:
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "deep auxiliary learning for visual localization and odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "billion scale similarity search with gpus"
    - "semantic visual localization"

Connected through hyperedge HE-7 (induced by source paper "pointnetvlad deep point cloud based retrieval for large scale place recognition"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "octnet learning deep 3d representations at high resolutions"
    - "tensorflow a system for large scale machine learning"
    - "3dmatch learning the matching of local 3d geometry in range scans"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "hybrid scene compression for visual localization"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "image based localization using lstms for structured feature correlation"
    - "random forests versus neural networks what s best for camera localization"
    - "dsac differentiable ransac for camera localization"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "netvlad cnn architecture for weakly supervised place recognition"

Question: Please tell me which class the center node 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)