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: "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
  Abstract: Neural networks can achieve extraordinary results on a wide variety of tasks. However, when they attempt to sequentially learn a number of tasks, they tend to learn the new task while destructively forgetting previous tasks. One solution to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of previous tasks. Our model combines pseudo-rehearsal with a deep generative model and a dual memory system, resulting in a method that does not demand additional storage requirements as the number of tasks increase. Our model iteratively learns three Atari 2600 games while retaining above human level performance on all three games and performing as well as a set of networks individually trained on the tasks. This result is achieved without revisiting or storing raw data from past tasks. Furthermore, previous state-of-the-art solutions demonstrate substantial forgetting compared to our model on these complex deep reinforcement learning tasks.

Connected through hyperedge HE-1 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal"):
  with members:
    - "closed loop memory gan for continual learning"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "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 "continual learning using world models for pseudo rehearsal"):
  with members:
    - "model based reinforcement learning for atari"
    - "complementary learning for overcoming catastrophic forgetting using experience replay"
    - "on first order meta learning algorithms"
    - "learning latent dynamics for planning from pixels"
    - "deep generative dual memory network for continual learning"
    - "recurrent world models facilitate policy evolution"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-3 (induced by source paper "locally weighted regression pseudo rehearsal for online learning of vehicle dynamics"):
  with members:
    - "autorally an open platform for aggressive autonomous driving"
    - "learning to adapt meta learning for model based control"
    - "continual learning with deep generative replay"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "fearnet brain inspired model for incremental learning"
    - "deep visual foresight for planning robot motion"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

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)