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: "curiosity driven experience prioritization via density estimation"
  Abstract: In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easily be imbalanced with respect to the achieved goal states. The problem of learning from imbalanced data is a well-known problem in supervised learning, but has not yet been thoroughly researched in RL. To address this problem, we propose a novel Curiosity-Driven Prioritization (CDP) framework to encourage the agent to over-sample those trajectories that have rare achieved goal states. The CDP framework mimics the human learning process and focuses more on relatively uncommon events. We evaluate our methods using the robotic environment provided by OpenAI Gym. The environment contains six robot manipulation tasks. In our experiments, we combined CDP with Deep Deterministic Policy Gradient (DDPG) with or without Hindsight Experience Replay (HER). The experimental results show that CDP improves both performance and sample-efficiency of reinforcement learning agents, compared to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "safe exploration in continuous action spaces"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "mutual information based state control for intrinsically motivated reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "dynamical distance learning for semi supervised and unsupervised skill discovery"
    - "efficient dialog policy learning via positive memory retention"
    - "large scale study of curiosity driven learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "soft hindsight experience replay"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "alphastar an evolutionary computation perspective"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "if maxent rl is the answer what is the question"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-5 (induced by source paper "curiosity driven multi criteria hindsight experience replay"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "exploration by random network distillation"
    - "improvements on hindsight learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "multi objective model based policy search for data efficient learning with sparse rewards"

Connected through hyperedge HE-6 (induced by source paper "skew fit state covering self supervised reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "temporal difference models model free deep rl for model based control"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-7 (induced by source paper "hindsight generative adversarial imitation learning"):
  with members:
    - "generative adversarial self imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "zero shot visual imitation"
    - "self imitation learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "generative adversarial imitation from observation"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-8 (induced by source paper "maximum entropy regularized multi goal reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"

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)