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 recursal solving the catastrophic forgetting problem in deep neural networks"
  Abstract: In general, neural networks are not currently capable of learning tasks in a sequential fashion. When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks. One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of the previous task/s. This is very effective for simple tasks. However, pseudo-rehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items. We accomplish pseudo-rehearsal by using a Generative Adversarial Network to generate items so that our deep network can learn to sequentially classify the CIFAR-10, SVHN and MNIST datasets. After training on all tasks, our network loses only 1.67% absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our model's performance is a substantial improvement compared to the current state of the art solution.

Connected through hyperedge HE-1 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-2 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-3 (induced by source paper "variational prototype replays for continual learning"):
  with members:
    - "few shot self reminder to overcome catastrophic forgetting"
    - "continuous learning in single incremental task scenarios"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "dynamic few shot visual learning without forgetting"
    - "memory aware synapses learning what not to forget"
    - "prototypical networks for few shot learning"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "efficient continual learning in neural networks with embedding regularization"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "progressive neural networks"
    - "neural graph machines learning neural networks using graphs"
    - "deep image prior"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-5 (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"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "continual reinforcement learning with complex synapses"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-6 (induced by source paper "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"):
  with members:
    - "continual state representation learning for reinforcement learning using generative replay"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "experience replay for continual learning"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "selective experience replay for lifelong learning"
    - "continual reinforcement learning with complex synapses"

Connected through hyperedge HE-7 (induced by source paper "generative replay with feedback connections as a general strategy for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "incremental classifier learning with generative adversarial networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "a strategy for an uncompromising incremental learner"
    - "scalable recollections for continual lifelong learning"

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)