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 "a novel geometry based algorithm for robust grasping in extreme clutter environment"):
  Abstract: This paper looks into the problem of grasping unknown objects in a cluttered environment using 3D point cloud data obtained from a range or an RGBD sensor. The objective is to identify graspable regions and detect suitable grasp poses from a single view, possibly, partial 3D point cloud without any apriori knowledge of the object geometry. The problem is solved in two steps: (1) identifying and segmenting various object surfaces and, (2) searching for suitable grasping handles on these surfaces by applying geometric constraints of the physical gripper. The first step is solved by using a modified version of region growing algorithm that uses a pair of thresholds for smoothness constraint on local surface normals to find natural boundaries of object surfaces. In this process, a novel concept of edge point is introduced that allows us to segment between different surfaces of the same object. The second step is solved by converting a 6D pose detection problem into a 1D linear search problem by projecting 3D cloud points onto the principal axes of the object surface. The graspable handles are then localized by applying physical constraints of the gripper. The resulting method allows us to grasp all kinds of objects including rectangular or box-type objects with flat surfaces which have been difficult so far to deal with in the grasping literature. The proposed method is simple and can be implemented in real-time and does not require any off-line training phase for finding these affordances. The improvements achieved is demonstrated through comparison with another state-of-the-art grasping algorithm on various publicly-available and self-created datasets.

Includes members:
  Member "high precision grasp pose detection in dense clutter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "deep learning a grasp function for grasping under gripper pose uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "using geometry to detect grasps in 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain independent unsupervised learning to grasp the novel objects")
  Member "supersizing self supervision learning to grasp from 50k tries and 700 robot hours":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "deep learning for detecting robotic grasps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "localizing grasp affordances in 3 d points clouds using taubin quadric fitting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generic primitive detection in point clouds using novel minimal quadric fits")

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)