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: "independently controllable factors"
  Abstract: It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

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"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on the sensory commutativity of action sequences for embodied agents"):
  with members:
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "exploration by random network distillation"
    - "learning actionable representations with goal conditioned policies"
    - "unsupervised emergence of spatial structure from sensorimotor prediction"
    - "a non linear approach to space dimension perception by a naive agent"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning an internal representation of the end effector configuration space"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-4 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-5 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "task agnostic dynamics priors for deep reinforcement learning"):
  with members:
    - "a study on overfitting in deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "flexible neural representation for physics prediction"
    - "decoupling dynamics and reward for transfer learning"
    - "model based value estimation for efficient model free reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "eigenoption discovery through the deep successor representation"
    - "visual interaction networks"

Connected through hyperedge HE-7 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-8 (induced by source paper "clic curriculum learning and imitation for object control in non rewarding environments"):
  with members:
    - "learning actionable representations with goal conditioned policies"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "large scale study of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "many goals reinforcement learning"
    - "curious intrinsically motivated modular multi goal 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.LG(Machine Learning)