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: "revisiting distillation and incremental classifier learning"
  Abstract: One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks. This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system. In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain conditions. To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature. We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e, it often leads to bias in classifiers. Finally, we propose a dynamic threshold moving algorithm that is able to successfully remove this bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results. Our implementation is available at this https URL.

Connected through hyperedge HE-1 (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"
    - "deesil deep shallow incremental learning"
    - "end to end incremental learning"

Connected through hyperedge HE-2 (induced by source paper "remind your neural network to prevent catastrophic forgetting"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "answer them all toward universal visual question answering models"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "bilinear attention networks"

Connected through hyperedge HE-3 (induced by source paper "class incremental learning via deep model consolidation"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "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"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-4 (induced by source paper "single net continual learning with progressive segmented training pst"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "efficient network construction through structural plasticity"
    - "automatic compiler based fpga accelerator for cnn training"
    - "regularize expand and compress multi task based lifelong learning via nonexpansive automl"
    - "large scale incremental learning"
    - "towards efficient neural networks on a chip joint hardware algorithm approaches"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-5 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "incremental classifier learning with generative adversarial networks"
    - "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-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"
    - "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"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-7 (induced by source paper "lifelong machine learning with deep streaming linear discriminant analysis"):
  with members:
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "efficient lifelong learning with a gem"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory efficient experience replay for streaming learning"

Connected through hyperedge HE-8 (induced by source paper "incremental learning for the detection and classification of gan generated images"):
  with members:
    - "source generator attribution via inversion"
    - "on the generalization of gan image forensics"
    - "exposing gan synthesized faces using landmark locations"
    - "do gans leave artificial fingerprints"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "detecting gan generated imagery using color cues"
    - "large scale gan training for high fidelity natural image synthesis"

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)