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: "continuous adaptation via meta learning in nonstationary and competitive environments"
  Abstract: Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"

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 "multi objective neural architecture search via non stationary policy gradient"):
  with members:
    - "nas evaluation is frustratingly hard"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "promp proximal meta policy search"

Connected through hyperedge HE-6 (induced by source paper "distribution agnostic model agnostic meta learning"):
  with members:
    - "online meta learning on non convex setting"
    - "online meta learning"
    - "agnostic federated learning"
    - "adversarial attacks on graph neural networks via meta learning"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "how to train your maml"

Connected through hyperedge HE-7 (induced by source paper "task augmentation by rotating for meta learning"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta learning with latent embedding optimization"
    - "learning to support exploiting structure information in support sets for one shot learning"
    - "dropblock a regularization method for convolutional networks"
    - "meta transfer learning for few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "dynamic few shot visual learning without forgetting"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-8 (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"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "decoupling dynamics and reward for transfer learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

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)