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 latent space dynamics for tactile servoing"
  Abstract: In order to achieve a dexterous robotic manipulation, we need to equip our robot with a tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper we specifically address the challenge of tactile servoing, i.e. given the current tactile sensing and a target/goal tactile sensing --for example being memorized from a successful task execution in the past--, what is the action that will bring the current tactile sensing to move closer towards the target tactile sensing at the next time step. We develop a data-driven approach to acquire a dynamics model for tactile servoing by learning from demonstration. Moreover, our method represents the tactile sensing information as to lie on a surface --or a 2D manifold-- and perform a manifold learning, making it applicable to any tactile skin geometry. As a proof of concept, we evaluate our method on a robot equipped with a tactile finger. A video demonstrating our approach can be seen in this https URL

Connected through hyperedge HE-1 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces"):
  with members:
    - "manipulating deformable objects by interleaving prediction planning and control"
    - "learning robotic manipulation through visual planning and acting"
    - "robot motion planning in learned latent spaces"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "robust locally linear controllable embedding"
    - "deep visual foresight for planning robot motion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning to manipulate object collections using grounded state representations"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "deep set prediction networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"

Connected through hyperedge HE-4 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  with members:
    - "stable concurrent controller composition for multi objective robotic tasks"
    - "representing robot task plans as robust logical dynamical systems"
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "manipulation by feel touch based control with deep predictive models"):
  with members:
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "stochastic adversarial video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "deep visual foresight for planning robot motion"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "learning to live life on the edge online learning for data efficient tactile contour following"):
  with members:
    - "shear invariant sliding contact perception with a soft tactile sensor"
    - "manipulation by feel touch based control with deep predictive models"
    - "principal components of touch"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"

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)