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 "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"):
  Abstract: 3D LiDARs and 2D cameras are increasingly being used alongside each other in sensor rigs for perception tasks. Before these sensors can be used to gather meaningful data, however, their extrinsics (and intrinsics) need to be accurately calibrated, as the performance of the sensor rig is extremely sensitive to these calibration parameters. A vast majority of existing calibration techniques require significant amounts of data and/or calibration targets and human effort, severely impacting their applicability in large-scale production systems. We address this gap with CalibNet: a geometrically supervised deep network capable of automatically estimating the 6-DoF rigid body transformation between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need for calibration targets, thereby resulting in significant savings in calibration efforts. During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i.e., we do not directly regress to the calibration parameters, for example). Instead, we train the network to predict calibration parameters that maximize the geometric and photometric consistency of the input images and point clouds. CalibNet learns to iteratively solve the underlying geometric problem and accurately predicts extrinsic calibration parameters for a wide range of mis-calibrations, without requiring retraining or domain adaptation. The project page is hosted at https://epiception.github.io/CalibNet

Includes members:
  Member "sbnet sparse blocks network for fast inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")
  Member "automatic extrinsic calibration for lidar stereo vehicle sensor setups":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soic semantic online initialization and calibration for lidar and camera")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "regnet multimodal sensor registration using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "undeepvo monocular visual odometry through unsupervised deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "a point set generation network for 3d object reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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.RO(Robotics)