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 "grim repr prioritising generating important features for pseudo rehearsal"):
  Abstract: Pseudo-rehearsal allows neural networks to learn a sequence of tasks without forgetting how to perform in earlier tasks. Preventing forgetting is achieved by introducing a generative network which can produce data from previously seen tasks so that it can be rehearsed along side learning the new task. This has been found to be effective in both supervised and reinforcement learning. Our current work aims to further prevent forgetting by encouraging the generator to accurately generate features important for task retention. More specifically, the generator is improved by introducing a second discriminator into the Generative Adversarial Network which learns to classify between real and fake items from the intermediate activation patterns that they produce when fed through a continual learning agent. Using Atari 2600 games, we experimentally find that improving the generator can considerably reduce catastrophic forgetting compared to the standard pseudo-rehearsal methods used in deep reinforcement learning. Furthermore, we propose normalising the Q-values taught to the long-term system as we observe this substantially reduces catastrophic forgetting by minimising the interference between tasks' reward functions.

Includes members:
  Member "closed loop memory gan for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controlled forgetting targeted stimulation and dopaminergic plasticity modulation for unsupervised lifelong learning in spiking neural networks")
  Member "generative adversarial networks and perceptual losses for video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep blind video super resolution")
  Member "continual state representation learning for reinforcement learning using generative replay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continual learning using world models for pseudo rehearsal")
  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 "continual reinforcement learning with complex synapses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "orthogonal gradient descent for continual learning")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)