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: "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
  Abstract: Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge. Here, we propose a new approach to sequential learning which leverages the recent discovery of adversarial examples. We use adversarial subspaces from previous tasks to enable learning of new tasks with less interference. We apply our method to sequentially learning to classify digits 0, 1, 2 (task 1), 4, 5, 6, (task 2), and 7, 8, 9 (task 3) in MNIST (disjoint MNIST task). We compare and combine our Adversarial Direction (AD) method with the recently proposed Elastic Weight Consolidation (EWC) method for sequential learning. We train each task for 20 epochs, which yields good initial performance (99.24% correct task 1 performance). After training task 2, and then task 3, both plain gradient descent (PGD) and EWC largely forget task 1 (task 1 accuracy 32.95% for PGD and 41.02% for EWC), while our combined approach (AD+EWC) still achieves 94.53% correct on task 1. We obtain similar results with a much more difficult disjoint CIFAR10 task, which to our knowledge had not been attempted before (70.10% initial task 1 performance, 67.73% after learning tasks 2 and 3 for AD+EWC, while PGD and EWC both fall to chance level). Our results suggest that AD+EWC can provide better sequential learning performance than either PGD or EWC.

Connected through hyperedge HE-1 (induced by source paper "large scale mobile app identification using deep learning"):
  with members:
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "multitask learning for network traffic classification"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "deep learning for encrypted traffic classification an overview"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "can t you hear me knocking identification of user actions on android apps via traffic analysis"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "multitask learning for network traffic classification"):
  with members:
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "deep learning for encrypted traffic classification an overview"

Connected through hyperedge HE-3 (induced by source paper "progressive memory banks for incremental domain adaptation"):
  with members:
    - "episodic memory in lifelong language learning"
    - "oracle order robust adaptive continual learning"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "memory augmented dialogue management for task oriented dialogue systems"
    - "efficient lifelong learning with a gem"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "reinforced continual learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

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)