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 "marginal replay vs conditional replay for continual learning"):
  Abstract: We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term "marginal replay") that generates samples independently of their class and assigns labels in a separate step. The main improvement in conditional replay is that labels for generated samples need not be inferred, which reduces the margin for error in complex continual classification learning tasks. We demonstrate the effectiveness of this approach using novel and standard benchmarks constructed from MNIST and FashionMNIST data, and compare to the regularization-based EWC method.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "a comprehensive application oriented study of catastrophic forgetting in dnns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks")
  Member "training discriminative models to evaluate generative ones":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining gold samples for conditional gans")
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "distillation techniques for pseudo rehearsal based incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative models from the perspective of continual learning")
  Member "rotate your networks better weight consolidation and less catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual universal object detection")
  Member "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild")
  Member "memory replay gans learning to generate images from new categories without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "continual universal object detection")

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.LG(Machine Learning)