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: "efficient transfer learning and online adaptation with latent variable models for continuous control"
  Abstract: Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.

Connected through hyperedge HE-1 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "reset free trial and error learning for robot damage recovery"
    - "on first order meta learning algorithms"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-2 (induced by source paper "generalized hidden parameter mdps transferable model based rl in a handful of trials"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "deepmdp learning continuous latent space models for representation learning"
    - "benchmarking model based reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "decoupling dynamics and reward for transfer learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-3 (induced by source paper "varibad a very good method for bayes adaptive deep rl via meta learning"):
  with members:
    - "meta reinforcement learning with task embedding and shared policy"
    - "meta learning of sequential strategies"
    - "meta amortized variational inference and learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta reinforcement learning as task inference"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-4 (induced by source paper "single episode policy transfer in reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "fingerprint policy optimisation for robust reinforcement learning"
    - "decoupling dynamics and reward for transfer learning"
    - "an overview of machine teaching"
    - "learning to explore with meta policy gradient"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

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)