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 "3dregnet a deep neural network for 3d point registration"):
  Abstract: We present 3DRegNet, a deep learning algorithm for the registration of 3D scans. With the recent emergence of inexpensive 3D commodity sensors, it would be beneficial to develop a learning based 3D registration algorithm. Given a set of 3D point correspondences, we build a deep neural network using deep residual layers and convolutional layers to achieve two tasks: (1) classification of the point correspondences into correct/incorrect ones, and (2) regression of the motion parameters that can align the scans into a common reference frame. 3DRegNet has several advantages over classical methods. First, since 3DRegNet works on point correspondences and not on the original scans, our approach is significantly faster than many conventional approaches. Second, we show that the algorithm can be extended for multi-view scenarios, i.e., simultaneous handling of the registration for more than two scans. In contrast to pose regression networks that employ four variables to represent rotation using quaternions, we use Lie algebra to represent the rotation using only three variables. Extensive experiments on two challenging datasets (i.e. ICL-NUIM and SUN3D) demonstrate that we outperform other methods and achieve state-of-the-art results. The code will be made available.

Includes members:
  Member "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association")
  Member "learning transformation synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning multiview 3d point cloud registration")
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "regnet learning the optimization of direct image to image pose registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing through learned errors for accurate sports field registration")
  Member "deepmapping unsupervised map estimation from multiple point clouds":
    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 "3dfeat net weakly supervised local 3d features for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "eigendecomposition free training of deep networks with zero eigenvalue based losses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning":
    also connected through other hyperedges:
      (none)

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)