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: "taking visual motion prediction to new heightfields"
  Abstract: While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and estimating the associated parameters. In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortunately, such approaches are unsuited for modeling complex real-world scenarios, where manually authoring relevant state spaces tend to be tedious and challenging. In this work, we investigate if neural networks can implicitly learn physical states of real-world mechanical processes only based on visual data while internally modeling non-homogeneous environment and in the process enable long-term physical extrapolation. We develop a recurrent neural network architecture for this task and also characterize resultant uncertainties in the form of evolving variance estimates. We evaluate our setup to extrapolate motion of rolling ball(s) on bowls of varying shape and orientation, and on arbitrary heightfields using only images as input. We report significant improvements over existing image-based methods both in terms of accuracy of predictions and complexity of scenarios; and report competitive performance with approaches that, unlike us, assume access to internal physical states.

Connected through hyperedge HE-1 (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-2 (induced by source paper "stochastic prediction of multi agent interactions from partial observations"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "graph networks as learnable physics engines for inference and control"
    - "stochastic adversarial video prediction"
    - "generative multi agent behavioral cloning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised intuitive physics from past experiences"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "neural allocentric intuitive physics prediction from real videos"
    - "graph networks as learnable physics engines for inference and control"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "unsupervised intuitive physics from visual observations"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "recurrent environment simulators"

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

Connected through hyperedge HE-5 (induced by source paper "densephysnet learning dense physical object representations via multi step dynamic interactions"):
  with members:
    - "environment probing interaction policies"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "interpretable intuitive physics model"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "visual interaction networks"
    - "preparing for the unknown learning a universal policy with online system identification"

Connected through hyperedge HE-6 (induced by source paper "answering visual what if questions from actions to predicted scene descriptions"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "dvqa understanding data visualizations via question answering"
    - "visual interaction networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-7 (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"
    - "propagation networks for model based control under partial observation"
    - "spnets differentiable fluid dynamics for deep neural networks"
    - "simple recurrent units for highly parallelizable recurrence"
    - "visual interaction networks"

Connected through hyperedge HE-8 (induced by source paper "propagation networks for model based control under partial observation"):
  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"
    - "simple recurrent units for highly parallelizable recurrence"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "neural message passing for quantum chemistry"

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.CV(Computer Vision and Pattern Recognition)