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 "deepmapping unsupervised map estimation from multiple point clouds"):
  Abstract: We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that "training" these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at this https URL.

Includes members:
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "deeptam deep tracking and mapping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "open3d a modern library for 3d data processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "codeslam learning a compact optimisable representation for dense visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "3dfeat net weakly supervised local 3d features for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "global pose estimation with an attention based recurrent network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry":
    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 "semantic visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "segmap segment based mapping and localization using data driven descriptors")

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)