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: "combining learned and analytical models for predicting action effects"
  Abstract: One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, these dynamics are described by physics-based analytical models, which may however be very hard to find for complex problems. More recently, we have seen learning approaches that can predict the effect of more complex physical interactions directly from sensory input. However, it is an open question how far these models generalize beyond their training data. In this work, we analyse how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a neural network to convert the raw sensory data into a suitable representation that can be consumed by the analytical model and compare this approach to using neural networks for both, perception and prediction. Our results show that the combined method outperforms the purely learned version in terms of accuracy and generalization to push actions not seen during training. It also performs comparable to the analytical model applied on ground truth input values, despite using raw sensory data as input.

Connected through hyperedge HE-1 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"):
  with members:
    - "meta reinforcement learning as task inference"
    - "attentive neural processes"
    - "conditional neural processes"
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "the perception distortion tradeoff"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-2 (induced by source paper "combining physical simulators and object based networks for control"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "universal planning networks"
    - "graph networks as learnable physics engines for inference and control"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "a data efficient approach to precise and controlled pushing"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-3 (induced by source paper "tunenet one shot residual tuning for system identification and sim to real robot task transfer"):
  with members:
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "residual policy learning"
    - "friction variability in auto collected dataset of planar pushing experiments and anisotropic friction"
    - "sim to real learning agile locomotion for quadruped robots"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-4 (induced by source paper "combining coarse and fine physics for manipulation using parallel in time integration"):
  with members:
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"

Connected through hyperedge HE-5 (induced by source paper "tossingbot learning to throw arbitrary objects with residual physics"):
  with members:
    - "residual policy learning"
    - "data augmented contact model for rigid body simulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "residual reinforcement learning for robot control"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "deep predictive policy training using reinforcement learning"
    - "learning data efficient rigid body contact models case study of planar impact"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning of state estimation for manipulating deformable linear objects"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "propagation networks for model based control under partial observation"
    - "unsupervised intuitive physics from visual observations"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-7 (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "parallelizing over artificial neural network training runs with multigrid"

Connected through hyperedge HE-8 (induced by source paper "online learning in planar pushing with combined prediction model"):
  with members:
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "a probabilistic data driven model for planar pushing"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning to poke by poking experiential learning of intuitive physics"

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)