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: "regnet multimodal sensor registration using deep neural networks"
  Abstract: In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28 degrees for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20 degrees.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation"):
  with members:
    - "fire unsupervised bi directional inter modality registration using deep networks"
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "image and spatial transformer networks for structure guided image registration"
    - "unsupervised deformable image registration using cycle consistent cnn"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "multimodal unsupervised image to image translation"
    - "deep learning based inter modality image registration supervised by intra modality similarity"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "monster awakening the mono in stereo"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "ga net guided aggregation net for end to end stereo matching"
    - "monocular depth estimation a survey"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "pyramid stereo matching network"

Connected through hyperedge HE-3 (induced by source paper "cmrnet camera to lidar map registration"):
  with members:
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "deconvolutional networks for point cloud vehicle detection and tracking in driving scenarios"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "geometry aware learning of maps for camera localization"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-4 (induced by source paper "targetless rotational auto calibration of radar and camera for intelligent transportation systems"):
  with members:
    - "calibnet self supervised extrinsic calibration using 3d spatial transformer networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "network in network"

Connected through hyperedge HE-5 (induced by source paper "siamese convolutional neural network for sub millimeter accurate camera pose estimation and visual servoing"):
  with members:
    - "exploring convolutional networks for end to end visual servoing"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to track at 100 fps with deep regression networks"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning to see by moving"

Connected through hyperedge HE-6 (induced by source paper "parn pyramidal affine regression networks for dense semantic correspondence"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "deformable convolutional networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "proposal flow semantic correspondences from object proposals"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-7 (induced by source paper "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "stacked hourglass networks for human pose estimation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "calibnet self supervised extrinsic calibration using 3d spatial transformer networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "lidar camera calibration using 3d 3d point correspondences"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "stacked hourglass networks for human pose estimation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep residual learning for image 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)