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: "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
  Abstract: An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by augmenting an analytical rigid-body simulator with a neural network that learns to model uncertainty as residuals. Combining symbolic, deterministic simulators with learnable, stochastic neural nets provides us with expressiveness, efficiency, and generalizability simultaneously. Our model outperforms both purely analytical and purely learned simulators consistently on real, standard benchmarks. Compared with methods that model uncertainty using Gaussian processes, our model runs much faster, generalizes better to new object shapes, and is able to characterize the complex distribution of object trajectories.

Connected through hyperedge HE-1 (induced by source paper "predicting the physical dynamics of unseen 3d objects"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning generalizable physical dynamics of 3d rigid objects"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-2 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "environment probing interaction policies"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "single episode policy transfer in reinforcement learning"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"

Connected through hyperedge HE-3 (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"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (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"
    - "a data efficient approach to precise and controlled pushing"
    - "combining learned and analytical models for predicting action effects"
    - "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-5 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"

Connected through hyperedge HE-6 (induced by source paper "vid2param modelling of dynamics parameters from video"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "active localization of gas leaks using fluid simulation"
    - "interpretable latent spaces for learning from demonstration"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-7 (induced by source paper "vid2param online system identification from video for robotics applications"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "interpretable latent spaces for learning from demonstration"
    - "neural lander stable drone landing control using learned dynamics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-8 (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"
    - "residual reinforcement learning for robot 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)