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 "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"):
  Abstract: Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of the suction grasp to resist an external gravity wrench. To characterize grasps, we estimate robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels. We use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN) to classify robust suction targets in point clouds containing a single object. We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When evaluated on novel objects that we categorize as Basic (prismatic or cylindrical), Typical (more complex geometry), and Adversarial (with few available suction-grasp points) Dex-Net 3.0 achieves success rates of 98$\%$, 82$\%$, and 58$\%$ respectively, improving to 81$\%$ in the latter case when the training set includes only adversarial objects. Code, datasets, and supplemental material can be found at this http URL .

Includes members:
  Member "deep learning a grasp function for grasping under gripper pose uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "learning hand eye coordination for robotic grasping with deep learning and large scale data collection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "a summary of team mit s approach to the amazon picking challenge 2015":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts")
  Member "team delft s robot winner of the amazon picking challenge 2016":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "supersizing self supervision learning to grasp from 50k tries and 700 robot hours":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")

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)