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: "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
  Abstract: This paper explores the problem of autonomous, in-hand regrasping--the problem of moving from an initial grasp on an object to a desired grasp using the dexterity of a robot's fingers. We propose a planner for this problem which alternates between finger gaiting, and in-grasp manipulation. Finger gaiting enables the robot to move a single finger to a new contact location on the object, while the remaining fingers stably hold the object. In-grasp manipulation moves the object to a new pose relative to the robot's palm, while maintaining the contact locations between the hand and object. Given the object's geometry (as a mesh), the hand's kinematic structure, and the initial and desired grasps, we plan a sequence of finger gaits and object reposing actions to reach the desired grasp without dropping the object. We propose an optimization based approach and report in-hand regrasping plans for 5 objects over 5 in-hand regrasp goals each. The plans generated by our planner are collision free and guarantee kinematic feasibility.

Connected through hyperedge HE-1 (induced by source paper "benchmarking in hand manipulation"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "in hand manipulation via motion cones"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "reinforcement learning for pivoting task"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"

Connected through hyperedge HE-2 (induced by source paper "learning continuous 3d reconstructions for geometrically aware grasping"):
  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"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "robust grasp planning over uncertain shape completions"
    - "modeling grasp type improves learning based grasp planning"
    - "robust learning of tactile force estimation through robot interaction"
    - "learning implicit fields for generative shape modeling"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "deep dynamics models for learning dexterous manipulation"):
  with members:
    - "exploring model based planning with policy networks"
    - "when to trust your model model based policy optimization"
    - "calibrated model based deep reinforcement learning"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-4 (induced by source paper "learning hierarchical control for robust in hand manipulation"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "residual reinforcement learning for robot control"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "real time robust finger gaits planning under object shape and dynamics uncertainties"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "light weight refinenet for real time semantic segmentation"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning postural synergies for categorical grasping through shape space registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-6 (induced by source paper "building 3d object models during manipulation by reconstruction aware trajectory optimization"):
  with members:
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "active object localization with deep reinforcement learning"
    - "simultaneous localization mapping and manipulation for unsupervised object discovery"

Connected through hyperedge HE-7 (induced by source paper "robust learning of tactile force estimation through robot interaction"):
  with members:
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning sensor feedback models from demonstrations via phase modulated neural networks"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "super convergence very fast training of residual networks using large learning rates"
    - "tensorflow a system for large scale machine learning"
    - "deep learning for tactile understanding from visual and haptic data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"):
  with members:
    - "probabilistic articulated real time tracking for robot manipulation"

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)