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: "less forgetting learning in deep neural networks"
  Abstract: A catastrophic forgetting problem makes deep neural networks forget the previously learned information, when learning data collected in new environments, such as by different sensors or in different light conditions. This paper presents a new method for alleviating the catastrophic forgetting problem. Unlike previous research, our method does not use any information from the source domain. Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments. Furthermore, we observed that the forgetting problem occurs between mini-batches when performing general training processes using stochastic gradient descent methods, and this problem is one of the factors that degrades generalization performance of the network. We also try to solve this problem using the proposed method. Finally, we show our less-forgetting learning method is also helpful to improve the performance of deep neural networks in terms of recognition rates.

Connected through hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-2 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"

Connected through hyperedge HE-3 (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-4 (induced by source paper "continual and multi task architecture search"):
  with members:
    - "the evolved transformer"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "efficient neural architecture search via parameter sharing"
    - "reinforced video captioning with entailment rewards"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "uncertainty guided continual learning with bayesian neural networks"):
  with members:
    - "large scale incremental learning"
    - "facilitating bayesian continual learning by natural gradients and stein gradients"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "task free continual learning"
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-6 (induced by source paper "incremental concept learning via online generative memory recall"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual lifelong learning with neural networks a review"
    - "high fidelity image generation with fewer labels"
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "large scale incremental learning"):
  with members:
    - "end to end incremental learning"
    - "a strategy for an uncompromising incremental learner"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "one shot face recognition by promoting underrepresented classes"
    - "encoder based lifelong learning"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-8 (induced by source paper "knowledge flow improve upon your teachers"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "progressive neural networks"
    - "analysis and optimization of convolutional neural network architectures"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emnist an extension of mnist to handwritten letters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"

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)