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: "learning to adapt meta learning for model based control"
  Abstract: Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but narrowly-learned policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to new situations as well as to perturbations. To enable sample-efficient meta-learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach trains a global model such that, when combined with recent data, the model can be be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, to a crippled leg, and in highly-dynamic environments.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (induced by source paper "hyper meta reinforcement learning with sparse reward"):
  with members:
    - "meta reinforcement learning with task embedding and shared policy"
    - "meta inverse reinforcement learning with probabilistic context variables"
    - "dealing with sparse rewards in reinforcement learning"
    - "mame model agnostic meta exploration"
    - "norml no reward meta learning"
    - "reward shaping via meta learning"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-3 (induced by source paper "intrinsic motivation for encouraging synergistic behavior"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "self supervised exploration via disagreement"
    - "efficient bimanual manipulation using learned task schemas"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "emergent coordination through competition"
    - "large scale study of curiosity driven learning"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "provably convergent policy gradient methods for model agnostic meta reinforcement learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "guided meta policy search"
    - "online meta learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "meta learning with implicit gradients"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-7 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta learning by the baldwin effect"

Connected through hyperedge HE-8 (induced by source paper "generalization in reinforcement learning with selective noise injection and information bottleneck"):
  with members:
    - "rogue gym a new challenge for generalization in reinforcement learning"
    - "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "natural environment benchmarks for reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in 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)