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: "overcoming catastrophic forgetting with hard attention to the task"
  Abstract: Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning. A hard attention mask is learned concurrently to every task, through stochastic gradient descent, and previous masks are exploited to condition such learning. We show that the proposed mechanism is effective for reducing catastrophic forgetting, cutting current rates by 45 to 80%. We also show that it is robust to different hyperparameter choices, and that it offers a number of monitoring capabilities. The approach features the possibility to control both the stability and compactness of the learned knowledge, which we believe makes it also attractive for online learning or network compression applications.

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 "incremental object detection via meta learning"):
  with members:
    - "meta curvature"
    - "rilod near real time incremental learning for object detection at the edge"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "meta learning without memorization"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "efficient lifelong learning with a gem"
    - "few shot object detection via feature reweighting"

Connected through hyperedge HE-3 (induced by source paper "continual universal object detection"):
  with members:
    - "distilling object detectors with fine grained feature imitation"
    - "continual lifelong learning with neural networks a review"
    - "towards universal object detection by domain attention"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "learning without memorizing"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-4 (induced by source paper "think locally act globally federated learning with local and global representations"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "improved adversarial learning for fair classification"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-5 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "triple memory networks a brain inspired method for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

Connected through hyperedge HE-7 (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"
    - "reinforced continual learning"
    - "darts differentiable architecture search"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-8 (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"
    - "reinforced continual learning"
    - "progressive neural 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)