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: "unsupervised intuitive physics from visual observations"
  Abstract: While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have relaxed such requirements by supplementing the model with an handcrafted physical simulator. Still, the resulting methods are unable to automatically learn new complex environments and to understand physical interactions within them. In this work, we demonstrated for the first time learning such predictors directly from raw visual observations and without relying on simulators. We do so in two steps: first, we learn to track mechanically-salient objects in videos using causality and equivariance, two unsupervised learning principles that do not require auto-encoding. Second, we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that in all such cases it is possible to learn reliable extrapolators of the object trajectories from raw videos alone, without any form of external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.

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 "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"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "recurrent environment simulators"
    - "neural expectation maximization"

Connected through hyperedge HE-3 (induced by source paper "structured object aware physics prediction for video modeling and planning"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "monet unsupervised scene decomposition and representation"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "relational action forecasting"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"

Connected through hyperedge HE-4 (induced by source paper "physics as inverse graphics joint unsupervised learning of objects and physics from video"):
  with members:
    - "unsupervised discovery of parts structure and dynamics"
    - "learning programmatically structured representations with perceptor gradients"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-5 (induced by source paper "learning generalizable physical dynamics of 3d rigid objects"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "physical primitive decomposition"
    - "interpretable intuitive physics model"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to represent mechanics via long term extrapolation and interpolation"
    - "unsupervised learning for physical interaction through video prediction"

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"
    - "propagation networks for model based control under partial observation"
    - "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 "sim to real transfer learning using robustified controllers in robotic tasks involving complex dynamics"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "hindsight experience replay"
    - "one shot visual imitation learning via meta learning"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "learning robust rewards with adversarial inverse reinforcement learning"

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)