human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "learning sensor feedback models from demonstrations via phase modulated neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning generalisable coupling terms for obstacle avoidance via low dimensional geometric descriptors")
  Member "riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "camera to robot pose estimation from a single image")
  Member "parametric manifold learning via sparse multidimensional scaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining on manifolds metric learning without labels")
  Member "learning to represent haptic feedback for partially observable tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks")
  Member "se3 pose nets structured deep dynamics models for visuomotor planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action for better prediction")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "learning dexterous manipulation policies from experience and imitation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solving rubik s cube with a robot hand")
  Member "learning to poke by poking experiential learning of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")

Question: Please tell me which class the source paper 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)