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: "learning 6 dof grasping and pick place using attention focus"
  Abstract: We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom -- 3D position and orientation. Our approach is to formulate the problem as a Markov decision process (MDP) with abstract yet generally applicable state and action representations. Finding a good solution to the MDP requires adding constraints on the allowed actions. We develop a specific set of constraints called hierarchical $\text{SE}(3)$ sampling (HSE3S) which causes the robot to learn a sequence of gazes to focus attention on the task-relevant parts of the scene. We demonstrate the effectiveness of our approach on three challenging pick-place tasks (with novel objects in clutter and nontrivial places) both in simulation and on a real robot, even though all training is done in simulation.

Connected through hyperedge HE-1 (induced by source paper "learning manipulation skills via hierarchical spatial attention"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "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"
    - "hindsight experience replay"

Connected through hyperedge HE-2 (induced by source paper "zero shot reinforcement learning with deep attention convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reinforcement learning with attention that works a self supervised approach"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "vmav c a deep attention based reinforcement learning algorithm for model based control"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "grasping in the wild learning 6dof closed loop grasping from low cost demonstrations"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"):
  with members:
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "on the continuity of rotation representations in neural networks"
    - "grasp pose detection in point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "towards precise robotic grasping by probabilistic post grasp displacement estimation"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-6 (induced by source paper "object finding in cluttered scenes using interactive perception"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "open3d a modern library for 3d data processing"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "interactive perception leveraging action in perception and perception in action"
    - "proximal policy optimization algorithms"
    - "revisiting active perception"

Connected through hyperedge HE-7 (induced by source paper "bounding box regression with uncertainty for accurate object detection"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "acquisition of localization confidence for accurate object detection"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning of active vision for manipulating objects under occlusions"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a dataset for developing and benchmarking active vision"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "asymmetric actor critic for image based robot learning"
    - "cognitive mapping and planning for visual navigation"

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.RO(Robotics)