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 unlabeled data in the wild"
  Abstract: Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a large stream of unlabeled data easily obtainable in the wild. In particular, we design a novel class-incremental learning scheme with (a) a new distillation loss, termed global distillation, (b) a learning strategy to avoid overfitting to the most recent task, and (c) a confidence-based sampling method to effectively leverage unlabeled external data. Our experimental results on various datasets, including CIFAR and ImageNet, demonstrate the superiority of the proposed methods over prior methods, particularly when a stream of unlabeled data is accessible: our method shows up to 15.8% higher accuracy and 46.5% less forgetting compared to the state-of-the-art method. The code is available at https://github.com/kibok90/iccv2019-inc.

Connected through hyperedge HE-1 (induced by source paper "remind your neural network to prevent catastrophic forgetting"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "answer them all toward universal visual question answering models"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "bilinear attention networks"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"

Connected through hyperedge HE-2 (induced by source paper "uncertainty guided continual learning with bayesian neural networks"):
  with members:
    - "large scale incremental learning"
    - "facilitating bayesian continual learning by natural gradients and stein gradients"
    - "task free continual learning"
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "lifelong machine learning with deep streaming linear discriminant analysis"):
  with members:
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "revisiting distillation and incremental classifier learning"
    - "efficient lifelong learning with a gem"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory efficient experience replay for streaming learning"

Connected through hyperedge HE-4 (induced by source paper "sequential mastery of multiple visual tasks networks naturally learn to learn and forget to forget"):
  with members:
    - "fine grained continual learning"
    - "continual lifelong learning with neural networks a review"
    - "task free continual learning"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "deep generative dual memory network for continual learning"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "a simple neural network module for relational reasoning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-5 (induced by source paper "three scenarios for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental classifier learning with generative adversarial networks"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "a strategy for an uncompromising incremental learner"

Connected through hyperedge HE-6 (induced by source paper "learning with long term remembering following the lead of mixed stochastic gradient"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "orthogonal gradient descent for continual learning"
    - "efficient lifelong learning with a gem"
    - "multi task learning as multi objective optimization"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory aware synapses learning what not to forget"
    - "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.CV(Computer Vision and Pattern Recognition)