human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning to remember a synaptic plasticity driven framework for continual learning"):
  Abstract: Models trained in the context of continual learning (CL) should be able to learn from a stream of data over an undefined period of time. The main challenges herein are: 1) maintaining old knowledge while simultaneously benefiting from it when learning new tasks, and 2) guaranteeing model scalability with a growing amount of data to learn from. In order to tackle these challenges, we introduce Dynamic Generative Memory (DGM) - a synaptic plasticity driven framework for continual learning. DGM relies on conditional generative adversarial networks with learnable connection plasticity realized with neural masking. Specifically, we evaluate two variants of neural masking: applied to (i) layer activations and (ii) to connection weights directly. Furthermore, we propose a dynamic network expansion mechanism that ensures sufficient model capacity to accommodate for continually incoming tasks. The amount of added capacity is determined dynamically from the learned binary mask. We evaluate DGM in the continual class-incremental setup on visual classification tasks.

Includes members:
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "adding new tasks to a single network with weight transformations using binary masks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental multi domain learning with network latent tensor factorization")
  Member "piggyback adapting a single network to multiple tasks by learning to mask weights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "memory replay gans learning to generate images from new categories without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continual universal object detection")
  Member "deep generative dual memory network for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an adaptive random path selection approach for incremental learning")
  Member "riemannian walk for incremental learning understanding forgetting and intransigence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "overcoming catastrophic forgetting with hard attention to the task":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "continual learning in generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial incremental learning")

Question: Please tell me which class the source paper 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.NE(Neural and Evolutionary Computing)