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: "reinforced continual learning"
  Abstract: Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.

Connected through hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning"):
  with members:
    - "learning to continually learn"
    - "fine grained continual learning"
    - "continuous meta learning without tasks"
    - "reinforcement learning in non stationary environments"
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "regularization shortcomings for continual learning"

Connected through hyperedge HE-2 (induced by source paper "a neural dirichlet process mixture model for task free continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "on tiny episodic memories in continual learning"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-3 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "lifelong learning with searchable extension units"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "multinomial distribution learning for effective neural architecture search"
    - "incremental classifier learning with generative adversarial networks"
    - "deep learning for classical japanese literature"
    - "darts differentiable architecture search"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-5 (induced by source paper "federated continual learning with adaptive parameter communication"):
  with members:
    - "federated learning with matched averaging"
    - "scalable and order robust continual learning with additive parameter decomposition"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "asynchronous online federated learning for edge devices"
    - "federated optimization for heterogeneous networks"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"

Connected through hyperedge HE-6 (induced by source paper "scalable and order robust continual learning with additive parameter decomposition"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "unsupervised representation learning by predicting image rotations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-7 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-8 (induced by source paper "ace adapting to changing environments for semantic segmentation"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "multimodal unsupervised image to image translation"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "end to end incremental learning"
    - "lifelong learning with dynamically expandable networks"

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)