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: "task agnostic dynamics priors for deep reinforcement learning"
  Abstract: While model-based deep reinforcement learning (RL) holds great promise for sample efficiency and generalization, learning an accurate dynamics model is often challenging and requires substantial interaction with the environment. A wide variety of domains have dynamics that share common foundations like the laws of classical mechanics, which are rarely exploited by existing algorithms. In fact, humans continuously acquire and use such dynamics priors to easily adapt to operating in new environments. In this work, we propose an approach to learn task-agnostic dynamics priors from videos and incorporate them into an RL agent. Our method involves pre-training a frame predictor on task-agnostic physics videos to initialize dynamics models (and fine-tune them) for unseen target environments. Our frame prediction architecture, SpatialNet, is designed specifically to capture localized physical phenomena and interactions. Our approach allows for both faster policy learning and convergence to better policies, outperforming competitive approaches on several different environments. We also demonstrate that incorporating this prior allows for more effective transfer between environments.

Connected through hyperedge HE-1 (induced by source paper "neuro evolutionary frameworks for generalized learning agents"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "model based reinforcement learning for atari"
    - "meta learning by the baldwin effect"
    - "investigating human priors for playing video games"
    - "a self replication basis for designing complex agents"
    - "hdltex hierarchical deep learning for text classification"
    - "a survey on multi task learning"
    - "concrete problems in ai safety"

Connected through hyperedge HE-2 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "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-3 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "when to trust your model model based policy optimization"):
  with members:
    - "model based reinforcement learning for atari"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"

Connected through hyperedge HE-5 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-6 (induced by source paper "bootstrapping the expressivity with model based planning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "exploring model based planning with policy networks"
    - "model based reinforcement learning for atari"
    - "fixup initialization residual learning without normalization"
    - "calibrated model based deep reinforcement learning"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-7 (induced by source paper "model based planning with energy based models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "exponential family estimation via adversarial dynamics embedding"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning dexterous in hand manipulation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

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.LG(Machine Learning)