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: "visual reinforcement learning with imagined goals"
  Abstract: For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised "practice" phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method. Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.

Connected through hyperedge HE-1 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "reward shaping via meta learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "generalized hindsight for reinforcement learning"):
  with members:
    - "sub policy adaptation for hierarchical reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deep drone racing learning agile flight in dynamic environments"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-3 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "monet unsupervised scene decomposition and representation"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "on the limitations of representing functions on sets"
    - "from language to goals inverse reinforcement learning for vision based instruction following"

Connected through hyperedge HE-4 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning"):
  with members:
    - "sqil imitation learning via reinforcement learning with sparse rewards"
    - "random expert distillation imitation learning via expert policy support estimation"
    - "generative predecessor models for sample efficient imitation learning"
    - "is the policy gradient a gradient"
    - "sqil imitation learning via regularized behavioral cloning"
    - "visual hindsight experience replay"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-5 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "model based reinforcement learning for atari"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-6 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning"):
  with members:
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "self supervised correspondence in visuomotor policy learning"
    - "learning representations by maximizing mutual information across views"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning actionable representations from visual observations"
    - "playing hard exploration games by watching youtube"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "sparse graphical memory for robust planning"):
  with members:
    - "learning world graphs to accelerate hierarchical reinforcement learning"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "mapping state space using landmarks for universal goal reaching"
    - "hallucinative topological memory for zero shot visual planning"
    - "temporal difference models model free deep rl for model based control"
    - "distributed distributional deterministic policy gradients"
    - "hindsight experience replay"

Connected through hyperedge HE-8 (induced by source paper "sufficiently accurate model learning"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "deepmind control suite"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "constrained policy optimization"
    - "memory augmented control networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)