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: "unsupervised meta learning for reinforcement learning"
  Abstract: Meta-learning is a powerful tool that builds on multi-task learning to learn how to quickly adapt a model to new tasks. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks. The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning. We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods. Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.

Connected through hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised curricula for visual meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "deep clustering for unsupervised learning of visual features"
    - "model based active exploration"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "general non linear bellman equations"
    - "on inductive biases in deep reinforcement learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-4 (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"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"

Connected through hyperedge HE-5 (induced by source paper "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"):
  with members:
    - "reinforcement learning for improving agent design"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "evolving multimodal robot behavior via many stepping stones with the combinatorial multi objective evolutionary algorithm"
    - "distributed prioritized experience replay"
    - "diversity is all you need learning skills without a reward function"
    - "episodic curiosity through reachability"
    - "coevolutionary neural population models"

Connected through hyperedge HE-6 (induced by source paper "unsupervised reinforcement learning of transferable meta skills for embodied navigation"):
  with members:
    - "on first order meta learning algorithms"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "diversity is all you need learning skills without a reward function"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-7 (induced by source paper "meta reinforcement learning with task embedding and shared policy"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "learning to explore with meta policy gradient"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "evolved policy gradients"
    - "learning to adapt meta learning for model based control"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-8 (induced by source paper "concurrent meta reinforcement learning"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "vain attentional multi agent predictive modeling"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

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)