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: "difference of normals as a multi scale operator in unorganized point clouds"
  Abstract: A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.

Connected through hyperedge HE-1 (induced by source paper "monocular trail detection and tracking aided by visual slam for small unmanned aerial vehicles"):
  with members:
    - "a survey on non filter based monocular visual slam systems"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-2 (induced by source paper "generic primitive detection in point clouds using novel minimal quadric fits"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "pcpnet learning local shape properties from raw point clouds"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "grasping unknown objects in clutter by superquadric representation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "real time 3d scene description using spheres cones and cylinders"
    - "a large dataset of object scans"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"

Connected through hyperedge HE-3 (induced by source paper "fast resampling of three dimensional point clouds via graphs"):
  with members:
    - "greedy sampling of graph signals"
    - "blind identification of graph filters"
    - "convergence analysis of the information matrix in gaussian belief propagation"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-4 (induced by source paper "object detection and motion planning for automated welding of tubular joints"):
  with members:
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "direct off line robot programming via a common cad package"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"):
  with members:
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "learning contact rich manipulation skills with guided policy search"
    - "synthesizing manipulation sequences for under specified tasks using unrolled markov random fields"
    - "deep learning for detecting robotic grasps"
    - "exploiting similarities among languages for machine translation"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

Connected through hyperedge HE-6 (induced by source paper "semanticpaint interactive 3d labeling and learning at your fingertips"):
  with members:
    - "imagespirit verbal guided image parsing"
    - "indoor semantic segmentation using depth information"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)