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: "promp proximal meta policy search"
  Abstract: Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL. Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients. By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-3 (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms"):
  with members:
    - "provably convergent policy gradient methods for model agnostic meta reinforcement learning"
    - "distribution agnostic model agnostic meta learning"
    - "es maml simple hessian free meta learning"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "online meta learning"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "meta curvature"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "autoaugment learning augmentation policies from data"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "curriculum in gradient based meta reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "mame model agnostic meta exploration"
    - "learning domain randomization distributions for transfer of locomotion policies"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-6 (induced by source paper "mghrl meta goal generation for hierarchical reinforcement learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hindsight experience replay"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (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"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "deep clustering for unsupervised learning of visual features"
    - "model based active exploration"

Connected through hyperedge HE-8 (induced by source paper "multimodal model agnostic meta learning via task aware modulation"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "on first order meta learning algorithms"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "meta continual learning"
    - "recasting gradient based meta learning as hierarchical bayes"

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)