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 sensor feedback models from demonstrations via phase modulated neural networks"
  Abstract: In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure –with movement phases as kernel centers– which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot.

Connected through hyperedge HE-1 (induced by source paper "learning generalisable coupling terms for obstacle avoidance via low dimensional geometric descriptors"):
  with members:
    - "learning and generalisation of primitives skills towards robust dual arm manipulation"
    - "learning feedback terms for reactive planning and control"
    - "multi target regression via input space expansion treating targets as inputs"

Connected through hyperedge HE-2 (induced by source paper "learning latent space dynamics for tactile servoing"):
  with members:
    - "riemannian motion policies"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "learning to represent haptic feedback for partially observable tasks"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning dexterous manipulation policies from experience and imitation"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (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"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "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-4 (induced by source paper "learning robust manipulation skills with guided policy search via generative motor reflexes"):
  with members:
    - "deep reinforcement learning for high precision assembly tasks"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "learning feedback terms for reactive planning and control"
    - "guided policy search as approximate mirror descent"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "learning contact rich manipulation skills with guided policy search"
    - "end to end training of deep visuomotor policies"

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)