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: "propagation networks for model based control under partial observation"
  Abstract: There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.

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 "learning to simulate complex physics with graph networks"):
  with members:
    - "learning to control pdes with differentiable physics"
    - "dynamic graph convolutional networks"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "hamiltonian graph networks with ode integrators"
    - "scale mlperf 0 6 models on google tpu v3 pods"
    - "difftaichi differentiable programming for physical simulation"
    - "graph networks as learnable physics engines for inference and control"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

Connected through hyperedge HE-3 (induced by source paper "belief regulated dual propagation nets for learning action effects on groups of articulated objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical dynamics"

Connected through hyperedge HE-4 (induced by source paper "clevrer collision events for video representation and reasoning"):
  with members:
    - "cater a diagnostic dataset for compositional actions and temporal reasoning"
    - "monet unsupervised scene decomposition and representation"
    - "cophy counterfactual learning of physical dynamics"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "learning compositional koopman operators for model based control"):
  with members:
    - "deep variational koopman models inferring koopman observations for uncertainty aware dynamics modeling and control"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "graph networks as learnable physics engines for inference and control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

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"
    - "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"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-7 (induced by source paper "belief regulated dual propagation nets for learning action effects on articulated multi part objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "video propagation networks"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"

Connected through hyperedge HE-8 (induced by source paper "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"):
  with members:
    - "universal planning networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "graph networks as learnable physics engines for inference and control"
    - "flexible neural representation for physics prediction"
    - "spnets differentiable fluid dynamics for deep neural networks"
    - "simple recurrent units for highly parallelizable recurrence"
    - "visual interaction networks"
    - "value prediction network"

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.AI(Artificial Intelligence)