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: "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
  Abstract: Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms. Here we introduce a modular approach to addressing these challenges in a continuous control environment, without using hand-crafted or supervised information. Our Curious Object-Based seaRch Agent (COBRA) uses task-free intrinsically motivated exploration and unsupervised learning to build object-based models of its environment and action space. Subsequently, it can learn a variety of tasks through model-based search in very few steps and excel on structured hold-out tests of policy robustness.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"
    - "combined reinforcement learning via abstract representations"

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"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "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 "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (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"
    - "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"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-5 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-6 (induced by source paper "unsupervised model selection for variational disentangled representation learning"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "on the fairness of disentangled representations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "life long disentangled representation learning with cross domain latent homologies"

Connected through hyperedge HE-7 (induced by source paper "joint learning of unsupervised object based perception and control"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised learning of object keypoints for perception and control"
    - "multi object representation learning with iterative variational inference"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "strategic object oriented reinforcement learning"
    - "relational deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "r sqair relational sequential attend infer repeat"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "a perspective on objects and systematic generalization in model based rl"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "learning to decompose and disentangle representations for video 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.LG(Machine Learning)