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: "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
  Abstract: Current learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping.

Connected through hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "learning to move with affordance maps"):
  with members:
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-4 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "model based reinforcement learning for atari"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "learning manipulation skills via hierarchical spatial attention"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "few shot goal inference for visuomotor learning and planning"
    - "deictic image maps an abstraction for learning pose invariant manipulation policies"
    - "multi view picking next best view reaching for improved grasping in clutter"

Connected through hyperedge HE-6 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds"):
  with members:
    - "object detection in 20 years a survey"
    - "relation shape convolutional neural network for point cloud analysis"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "a real time robotic grasp approach with oriented anchor box"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-7 (induced by source paper "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-8 (induced by source paper "multi fingered grasp planning via inference in deep neural networks"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "generating grasp poses for a high dof gripper using neural networks"
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "modeling grasp type improves learning based grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "robotic grasp detection using deep convolutional neural networks"

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.LG(Machine Learning)