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 "associating grasping with convolutional neural network features"):
  Abstract: In this work, we provide a solution for pre-shaping a human-like robot hand for grasping based on visual information. Our approach uses convolutional neural networks (CNNs) to define a mapping between images and grasps. Applying CNNs to robotics applications is non-trivial for two reasons. First, collecting enough robot data to train a CNN at the same scale as the models trained in the vision community is extremely difficult. In this work, we demonstrate that by using a pre-trained CNN, a small set of grasping examples is sufficient for generalizing across different objects of similar shapes. Second, the final output of a CNN contains little location information of the observed object, which is essential for the robot to manipulate the object. We take advantage of the hierarchical nature of CNN layers and identify the 3D positions of features that capture the hierarchical support relations between filters in different CNN layers using an approach we call targeted backpropagation. Targeted backpropagation traces the activation of higher level features in a CNN backwards through the network to discover the locations in the observation that were responsible for making them fire, thus localizing important structures that are manipulable in the environment. We show that this approach outperforms approaches without targeted backpropagation in a cluttered scene. We further implement a hierarchical controller that controls fingers and palms based on features located in different layers of the CNN for pre-shaping the robot hand and demonstrate that this approach outperforms a point cloud based approach on a grasping task on Robonaut-2.

Includes members:
  Member "understanding neural networks through deep visualization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "deep spatial autoencoders for visuomotor learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents")
  Member "supersizing self supervision learning to grasp from 50k tries and 700 robot hours":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "analyzing the performance of multilayer neural networks for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leep a new measure to evaluate transferability of learned representations")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep learning for detecting robotic grasps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "visualizing and understanding convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)