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 feedback terms for reactive planning and control"
  Abstract: With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-changing human environments requires reactive motion plans. Reactivity can be accomplished through re-planning, e.g. model-predictive control, or through a reactive feedback policy that modifies on-going behavior in response to sensory events. In this paper, we investigate how to use machine learning to add reactivity to a previously learned nominal skilled behavior. We approach this by learning a reactive modification term for movement plans represented by nonlinear differential equations. In particular, we use dynamic movement primitives (DMPs) to represent a skill and a neural network to learn a reactive policy from human demonstrations. We use the well explored domain of obstacle avoidance for robot manipulation as a test bed. Our approach demonstrates how a neural network can be combined with physical insights to ensure robust behavior across different obstacle settings and movement durations. Evaluations on an anthropomorphic robotic system demonstrate the effectiveness of our work.

Connected through hyperedge HE-1 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"

Connected through hyperedge HE-2 (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 sensor feedback models from demonstrations via phase modulated neural networks"
    - "multi target regression via input space expansion treating targets as inputs"

Connected through hyperedge HE-3 (induced by source paper "learning to adapt meta learning for model based control"):
  with members:
    - "model ensemble trust region policy optimization"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to learn meta critic networks for sample efficient learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "rapid adaptation with conditionally shifted neurons"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-4 (induced by source paper "learning to adapt in dynamic real world environments through meta reinforcement learning"):
  with members:
    - "model ensemble trust region policy optimization"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to learn meta critic networks for sample efficient learning"
    - "learning image conditioned dynamics models for control of under actuated legged millirobots"
    - "structure learning in motor control a deep reinforcement learning model"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-5 (induced by source paper "learning robust manipulation skills with guided policy search via generative motor reflexes"):
  with members:
    - "learning sensor feedback models from demonstrations via phase modulated neural networks"
    - "deep reinforcement learning for high precision assembly tasks"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning sensor feedback models from demonstrations via phase modulated neural networks"):
  with members:
    - "learning to represent haptic feedback for partially observable tasks"

Connected through hyperedge HE-7 (induced by source paper "real time perception meets reactive motion generation"):
  with members:
    - "riemannian motion policies"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "idrm humanoid motion planning with real time end pose selection in complex environments"
    - "analysis and observations from the first amazon picking challenge"
    - "doomed direct online optimization of modeling errors in dynamics"
    - "end to end training of deep visuomotor policies"
    - "probabilistic object tracking using a range camera"

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)