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: "supportnet solving catastrophic forgetting in class incremental learning with support data"
  Abstract: A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting. Here we propose a novel method, SupportNet, to efficiently and effectively solve the catastrophic forgetting problem in the class incremental learning scenario. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to stabilize the learned representation and ensure the robustness of the learned model. We validate our method with comprehensive experiments on various tasks, which show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and even reaches similar performance as the deep learning model trained from scratch on both old and new data. Our program is accessible at: this https URL

Connected through hyperedge HE-1 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "continual learning with tiny episodic memories"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "slimmable neural networks"

Connected through hyperedge HE-2 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "spectral normalization for generative adversarial networks"
    - "hierarchical novelty detection for visual object recognition"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "exploring the limits of weakly supervised pretraining"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-3 (induced by source paper "m2kd multi model and multi level knowledge distillation for incremental learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "learning without memorizing"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "end to end incremental learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"):
  with members:
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "darts differentiable architecture search"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-5 (induced by source paper "overcoming catastrophic forgetting by generative regularization"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "exponential family estimation via adversarial dynamics embedding"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-6 (induced by source paper "incremental learning for semantic segmentation of large scale remote sensing data"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "reinforced continual learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "end to end incremental learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-7 (induced by source paper "on the decision boundary of deep neural networks"):
  with members:
    - "stronger generalization bounds for deep nets via a compression approach"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "the loss surface of deep and wide neural networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "theory ii landscape of the empirical risk in deep learning"
    - "convergence analysis of two layer neural networks with relu activation"

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.NE(Neural and Evolutionary Computing)