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: "lifelong gan continual learning for conditional image generation"
  Abstract: Lifelong learning is challenging for deep neural networks due to their susceptibility to catastrophic forgetting. Catastrophic forgetting occurs when a trained network is not able to maintain its ability to accomplish previously learned tasks when it is trained to perform new tasks. We study the problem of lifelong learning for generative models, extending a trained network to new conditional generation tasks without forgetting previous tasks, while assuming access to the training data for the current task only. In contrast to state-of-the-art memory replay based approaches which are limited to label-conditioned image generation tasks, a more generic framework for continual learning of generative models under different conditional image generation settings is proposed in this paper. Lifelong GAN employs knowledge distillation to transfer learned knowledge from previous networks to the new network. This makes it possible to perform image-conditioned generation tasks in a lifelong learning setting. We validate Lifelong GAN for both image-conditioned and label-conditioned generation tasks, and provide qualitative and quantitative results to show the generality and effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "dissecting catastrophic forgetting in continual learning by deep visualization"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "contcap a comprehensive framework for continual image captioning"
    - "a unified framework for lifelong learning in deep neural networks"
    - "toward understanding catastrophic forgetting in continual learning"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "an empirical study of example forgetting during deep neural network learning"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

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"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "generative models from the perspective of 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 "similarity preserving knowledge distillation"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "cinic 10 is not imagenet or cifar 10"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation by on the fly native ensemble"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "syq learning symmetric quantization for efficient deep 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)