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: "generative models from the perspective of continual learning"
  Abstract: Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning. We used two quantitative metrics to estimate the generation quality and memory ability. We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10). We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge. Our code is available online \footnote{\url{this https URL\_Continual\_Learning}}.

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 "focl feature oriented continual learning for generative models"):
  with members:
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "continual learning in generative adversarial nets"
    - "modulating early visual processing by language"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "spectral normalization for generative adversarial networks"
    - "hierarchical novelty detection for visual object recognition"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "exploring the limits of weakly supervised pretraining"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"

Connected through hyperedge HE-4 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-5 (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"
    - "continual learning with tiny episodic memories"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "discorl continual reinforcement learning via policy distillation"):
  with members:
    - "continual learning for robotics"
    - "s trigger continual state representation learning via self triggered generative replay"
    - "continual reinforcement learning in 3d non stationary environments"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"

Connected through hyperedge HE-7 (induced by source paper "regularization shortcomings for continual learning"):
  with members:
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "continual learning for robotics definition framework learning strategies opportunities and challenges"
    - "online continual learning with maximally interfered retrieval"
    - "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"
    - "discorl continual reinforcement learning via policy distillation"
    - "deep learning for classical japanese literature"

Connected through hyperedge HE-8 (induced by source paper "online continual learning with maximally interfered retrieval"):
  with members:
    - "continual learning with tiny episodic memories"
    - "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"

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)