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 physical simulators and object based networks for control"
  Abstract: Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.

Connected through hyperedge HE-1 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-2 (induced by source paper "learning to manipulate object collections using grounded state representations"):
  with members:
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "deep set prediction networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-3 (induced by source paper "blending diverse physical priors with neural networks"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "block coordinate regularization by denoising"
    - "physics based neural networks for shape from polarization"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "deep learning approach for fourier ptychography microscopy"
    - "learning to separate multiple illuminants in a single image"

Connected through hyperedge HE-4 (induced by source paper "accurate vision based manipulation through contact reasoning"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "in hand manipulation via motion cones"
    - "self supervised visual planning with temporal skip connections"
    - "reactive planar manipulation with convex hybrid mpc"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

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)