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: "end to end incremental learning"
  Abstract: Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model -a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "incremental object detection via meta learning"):
  with members:
    - "meta curvature"
    - "rilod near real time incremental learning for object detection at the edge"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "meta learning without memorization"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "efficient lifelong learning with a gem"
    - "few shot object detection via feature reweighting"

Connected through hyperedge HE-2 (induced by source paper "modeling the background for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale incremental learning"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "learn the new keep the old extending pretrained models with new anatomy and images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "scail classifier weights scaling for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "do better imagenet models transfer better"
    - "revisiting distillation and incremental classifier learning"
    - "deesil deep shallow incremental learning"

Connected through hyperedge HE-4 (induced by source paper "an adaptive random path selection approach for incremental learning"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual lifelong learning with neural networks a review"
    - "exploring randomly wired neural networks for image recognition"
    - "random path selection for incremental learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"

Connected through hyperedge HE-5 (induced by source paper "brain inspired model for incremental learning using a few examples"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "large scale incremental learning"
    - "centroid based scene classification cbsc using deep features and clustering for rgb d indoor scene classification"
    - "cbcl brain inspired model for rgb d indoor scene classification"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "deesil deep shallow incremental learning"

Connected through hyperedge HE-6 (induced by source paper "adversarial incremental learning"):
  with members:
    - "large scale incremental learning"
    - "attending to discriminative certainty for domain adaptation"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "triple memory networks a brain inspired method for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

Connected through hyperedge HE-8 (induced by source paper "mnemonics training multi class incremental learning without forgetting"):
  with members:
    - "large scale incremental learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "pytorch an imperative style high performance deep learning library"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "meta transfer learning for few shot learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "a strategy for an uncompromising incremental learner"

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.CV(Computer Vision and Pattern Recognition)