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: "continual learning with tiny episodic memories"
  Abstract: Learning with less supervision is a major challenge in artificial intelligence. One sensible approach to decrease the amount of supervision is to leverage prior experience and transfer knowledge from tasks seen in the past. However, a necessary condition for a successful transfer is the ability to remember how to perform previous tasks. The Continual Learning (CL) setting, whereby an agent learns from a stream of tasks without seeing any example twice, is an ideal framework to investigate how to accrue such knowledge. In this work, we consider supervised learning tasks and methods that leverage a very small episodic memory for continual learning. Through an extensive empirical analysis across four benchmark datasets adapted to CL, we observe that a very simple baseline, which jointly trains on both examples from the current task as well as examples stored in the memory, outperforms state-of-the-art CL approaches with and without episodic memory. Surprisingly, repeated learning over tiny episodic memories does not harm generalization on past tasks, as joint training on data from subsequent tasks acts like a data dependent regularizer. We discuss and evaluate different approaches to write into the memory. Most notably, reservoir sampling works remarkably well across the board, except when the memory size is extremely small. In this case, writing strategies that guarantee an equal representation of all classes work better. Overall, these methods should be considered as a strong baseline candidate when benchmarking new CL approaches

Connected through hyperedge HE-1 (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 classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-2 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "slimmable neural networks"

Connected through hyperedge HE-3 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "three scenarios for continual learning"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-5 (induced by source paper "using hindsight to anchor past knowledge in continual learning"):
  with members:
    - "dataset distillation"
    - "on first order meta learning algorithms"
    - "task free continual learning"
    - "reptile a scalable metalearning algorithm"
    - "modular meta learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"

Connected through hyperedge HE-6 (induced by source paper "online learned continual compression with stacked quantization modules"):
  with members:
    - "decoupled greedy learning of cnns"
    - "generating diverse high fidelity images with vq vae 2"
    - "unsupervised state representation learning in atari"
    - "compressing the input for cnns with the first order scattering transform"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "deep generative modeling of lidar data"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-7 (induced by source paper "online continual learning with maximally interfered retrieval"):
  with members:
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "marginal replay vs conditional replay for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative models from the perspective of continual learning"

Connected through hyperedge HE-8 (induced by source paper "meta learning representations for continual learning"):
  with members:
    - "gradient based sample selection for online continual learning"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "meta learning update rules for unsupervised representation learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "the utility of sparse representations for control in reinforcement learning"
    - "deep online learning via meta learning continual adaptation for model based rl"

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)