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: "kpam keypoint affordances for category level robotic manipulation"
  Abstract: We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typically specify the desired configuration as a target 6-DOF pose and rely on explicitly estimating the pose of the manipulated objects. However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e.g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle. Hence we propose a novel formulation of category-level manipulation that uses semantic 3D keypoints as the object representation. This keypoint representation enables a simple and interpretable specification of the manipulation target as geometric costs and constraints on the keypoints, which flexibly generalizes existing pose-based manipulation methods. Using this formulation, we factor the manipulation policy into instance segmentation, 3D keypoint detection, optimization-based robot action planning and local dense-geometry-based action execution. This factorization allows us to leverage advances in these sub-problems and combine them into a general and effective perception-to-action manipulation pipeline. Our pipeline is robust to large intra-category shape variation and topology changes as the keypoint representation ignores task-irrelevant geometric details. Extensive hardware experiments demonstrate our method can reliably accomplish tasks with never-before seen objects in a category, such as placing shoes and mugs with significant shape variation into category level target configurations.

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"
    - "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"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-3 (induced by source paper "robotic grasping through combined image based grasp proposal and 3d reconstruction"):
  with members:
    - "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pixels to plans learning non prehensile manipulation by imitating a planner"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "higher order function networks for learning composable 3d object representations"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "open3d a modern library for 3d data processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-4 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta learning by the baldwin effect"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "graph structured visual imitation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-6 (induced by source paper "6 pack category level 6d pose tracker with anchor based keypoints"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "cornernet lite efficient keypoint based object detection"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "open3d a modern library for 3d data processing"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-7 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"):
  with members:
    - "inferring occluded geometry improves performance when retrieving an object from dense clutter"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "object placement planning and optimization for robot manipulators"
    - "robust grasp planning over uncertain shape completions"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"

Connected through hyperedge HE-8 (induced by source paper "keto learning keypoint representations for tool manipulation"):
  with members:
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "affordancenet an end to end deep learning approach for object affordance detection"

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)