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: "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"
  Abstract: We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.

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 "improvisation through physical understanding using novel objects as tools with visual foresight"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "zero shot visual imitation"
    - "deep imitative models for flexible inference planning and control"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "stochastic adversarial video prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "visual robot task planning"

Connected through hyperedge HE-3 (induced by source paper "learning to estimate pose and shape of hand held objects from rgb images"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "learning to estimate 3d hand pose from single rgb images"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "bayesian optimization in variational latent spaces with dynamic compression"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "zero shot skill composition and simulation to real transfer by learning task representations"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-5 (induced by source paper "learning task oriented grasping from human activity datasets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "analysis of hand segmentation in the wild"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-6 (induced by source paper "human visual understanding for cognition and manipulation a primer for the roboticist"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "the curious robot learning visual representations via physical interactions"
    - "deep learning for tactile understanding from visual and haptic data"
    - "real time grasp detection using convolutional neural networks"
    - "data driven grasp synthesis a survey"

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)