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 a comparative study on how to defy forgetting in classification tasks"
  Abstract: Artificial neural networks thrive in solving the classification problem for a particular rigid task, where the network resembles a static entity of knowledge, acquired through generalized learning behaviour from a distinct training phase. However, endeavours to extend this knowledge without targeting the original task usually result in a catastrophic forgetting of this task. Continual learning shifts this paradigm towards a network that can continually accumulate knowledge over different tasks without the need for retraining from scratch, with methods in particular aiming to alleviate forgetting. We focus on task-incremental classification, where tasks arrive in a batch-like fashion, and are delineated by clear boundaries. Our main contributions concern 1) a taxonomy and extensive overview of the state-of-the-art, 2) a novel framework to continually determine stability-plasticity trade-off of the continual learner, 3) a comprehensive experimental comparison of 10 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize which method performs best, both on balanced Tiny Imagenet and a large-scale unbalanced iNaturalist datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.

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 few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "boosting few shot visual learning with self supervision"
    - "large scale long tailed recognition in an open world"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "objects as points"
    - "image deformation meta networks for one shot learning"

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"
    - "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"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-4 (induced by source paper "modeling the background for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale incremental learning"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "learn the new keep the old extending pretrained models with new anatomy and images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

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"
    - "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-6 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "on training recurrent neural networks for lifelong learning"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks"):
  with members:
    - "three scenarios for continual learning"
    - "task free continual learning"
    - "efficient lifelong learning with a gem"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

Connected through hyperedge HE-8 (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"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "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"

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)