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 in generative adversarial nets"
  Abstract: Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.

Connected through hyperedge HE-1 (induced by source paper "adversarial incremental learning"):
  with members:
    - "large scale incremental learning"
    - "attending to discriminative certainty for domain adaptation"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "end to end incremental learning"
    - "memory aware synapses learning what not to forget"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (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"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

Connected through hyperedge HE-3 (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"
    - "generative models from the perspective of continual learning"
    - "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-4 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "continual learning with tiny episodic memories"
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"

Connected through hyperedge HE-5 (induced by source paper "lifelong gan continual learning for conditional image generation"):
  with members:
    - "model compression via distillation and quantization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "memory aware synapses learning what not to forget"
    - "learning to generate long term future via hierarchical prediction"

Connected through hyperedge HE-6 (induced by source paper "learning to remember a synaptic plasticity driven framework for continual learning"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "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"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-7 (induced by source paper "boovae a scalable framework for continual vae learning under boosting approach"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "inference suboptimality in variational autoencoders"
    - "progressive neural networks"
    - "vae with a vampprior"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-8 (induced by source paper "self supervised gans via auxiliary rotation loss"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "cgans with projection discriminator"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the gan landscape losses architectures regularization and normalization"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "on catastrophic forgetting and mode collapse in generative adversarial networks"
    - "deep clustering for unsupervised learning of visual features"
    - "spectral normalization for generative adversarial 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)