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: "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
  Abstract: Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting problem. This paper presents a conceptually simple yet general and effective framework for handling catastrophic forgetting in continual learning with DNNs. The proposed method consists of two components: a neural structure optimization component and a parameter learning and/or fine-tuning component. By separating the explicit neural structure learning and the parameter estimation, not only is the proposed method capable of evolving neural structures in an intuitively meaningful way, but also shows strong capabilities of alleviating catastrophic forgetting in experiments. Furthermore, the proposed method outperforms all other baselines on the permuted MNIST dataset, the split CIFAR100 dataset and the Visual Domain Decathlon dataset in continual learning setting.

Connected through hyperedge HE-1 (induced by source paper "a neural dirichlet process mixture model for task free continual learning"):
  with members:
    - "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"
    - "efficient lifelong learning with a gem"

Connected through hyperedge HE-2 (induced by source paper "structured compression and sharing of representational space for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "a low effort approach to structured cnn design using pca"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-3 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-4 (induced by source paper "lifelong learning with searchable extension units"):
  with members:
    - "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"
    - "reinforced continual learning"
    - "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 "scalable and order robust continual learning with additive parameter decomposition"):
  with members:
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "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-6 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "continual and multi task reinforcement learning with shared episodic memory"

Connected through hyperedge HE-7 (induced by source paper "learning to recommend via meta parameter partition"):
  with members:
    - "online meta learning"
    - "on first order meta learning algorithms"
    - "deep reinforcement learning for page wise recommendations"
    - "generative adversarial user model for reinforcement learning based recommendation system"
    - "federated meta learning for recommendation"
    - "federated meta learning with fast convergence and efficient communication"
    - "deep reinforcement learning for list wise recommendations"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-8 (induced by source paper "a unified framework for lifelong learning in deep neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "efficient lifelong learning with a gem"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

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)