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 "generative replay with feedback connections as a general strategy for continual learning"):
  Abstract: A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one. Numerous methods for alleviating catastrophic forgetting are currently being proposed, but differences in evaluation protocols make it difficult to directly compare their performance. To enable more meaningful comparisons, here we identified three distinct scenarios for continual learning based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as "soft targets") achieved superior performance in all three scenarios. Addressing the issue of efficiency, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback or backward connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.

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 "three scenarios for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "triple memory networks a brain inspired method for continual learning")
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to continually learn")
  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 "pseudo recursal solving the catastrophic forgetting problem in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks")
  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 "a strategy for an uncompromising incremental learner":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep 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.LG(Machine Learning)