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: "densephysnet learning dense physical object representations via multi step dynamic interactions"
  Abstract: We study the problem of learning physical object representations for robot manipulation. Understanding object physics is critical for successful object manipulation, but also challenging because physical object properties can rarely be inferred from the object's static appearance. In this paper, we propose DensePhysNet, a system that actively executes a sequence of dynamic interactions (e.g., sliding and colliding), and uses a deep predictive model over its visual observations to learn dense, pixel-wise representations that reflect the physical properties of observed objects. Our experiments in both simulation and real settings demonstrate that the learned representations carry rich physical information, and can directly be used to decode physical object properties such as friction and mass. The use of dense representation enables DensePhysNet to generalize well to novel scenes with more objects than in training. With knowledge of object physics, the learned representation also leads to more accurate and efficient manipulation in downstream tasks than the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "predicting the physical dynamics of unseen 3d objects"):
  with members:
    - "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"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

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"
    - "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"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-3 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-4 (induced by source paper "object centric forward modeling for model predictive control"):
  with members:
    - "compositional video prediction"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "learning to fly by crashing"

Connected through hyperedge HE-5 (induced by source paper "grasping in the wild learning 6dof closed loop grasping from low cost demonstrations"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "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"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-7 (induced by source paper "estimating mass distribution of articulated objects through physical interaction"):
  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"
    - "learning manipulation under physics constraints with visual perception"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "flexible neural representation for physics prediction"

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)