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: "closed loop memory gan for continual learning"
  Abstract: Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting. Generative models have been explored as a means to approximate the distribution of old tasks and bypass storage of real data. Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity. We evaluate incremental class learning using a notoriously hard paradigm, single-headed learning, in which each task is a disjoint subset of classes in the overall dataset, and performance is evaluated on all previous classes. First, we show that when constructing a dynamic memory unit to preserve sample heterogeneity, model performance asymptotically approaches training on the full dataset. We then show that using a stochastic generator to continuously output fresh new images during training increases performance significantly further meanwhile generating quality images. We compare our approach to several baselines including fine-tuning by gradient descent (FGD), Elastic Weight Consolidation (EWC), Deep Generative Replay (DGR) and Memory Replay GAN (MeRGAN). Our method has very low long-term memory cost, the memory unit, as well as negligible intermediate memory storage.

Connected through hyperedge HE-1 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal"):
  with members:
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "continual reinforcement learning with complex synapses"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-2 (induced by source paper "controlled forgetting targeted stimulation and dopaminergic plasticity modulation for unsupervised lifelong learning in spiking neural networks"):
  with members:
    - "continual learning with self organizing maps"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"

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)