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 to represent haptic feedback for partially observable tasks"
  Abstract: The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.

Connected through hyperedge HE-1 (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"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-2 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "grasp pose detection in point clouds"
    - "learning end to end multimodal sensor policies for autonomous navigation"

Connected through hyperedge HE-3 (induced by source paper "transferable force torque dynamics model for peg in hole task"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "deep reinforcement learning for high precision assembly tasks"
    - "guided policy search as approximate mirror descent"
    - "asynchronous methods for deep reinforcement learning"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "state representation learning for control an overview"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "stochastic variational video prediction"
    - "deep multimodal representation learning from temporal data"

Connected through hyperedge HE-5 (induced by source paper "learning awareness models"):
  with members:
    - "distributed prioritized experience replay"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "parameter space noise for exploration"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "the consciousness prior"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-6 (induced by source paper "learning to grasp without seeing"):
  with members:
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "cassl curriculum accelerated self supervised learning"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-7 (induced by source paper "learning latent space dynamics for tactile servoing"):
  with members:
    - "learning sensor feedback models from demonstrations via phase modulated neural networks"
    - "riemannian motion policies"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "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-8 (induced by source paper "learning sensor feedback models from demonstrations via phase modulated neural networks"):
  with members:
    - "learning feedback terms for reactive planning and control"

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)