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: "incremental classifier learning with generative adversarial networks"
  Abstract: In this paper, we address the incremental classifier learning problem, which suffers from catastrophic forgetting. The main reason for catastrophic forgetting is that the past data are not available during learning. Typical approaches keep some exemplars for the past classes and use distillation regularization to retain the classification capability on the past classes and balance the past and new classes. However, there are four main problems with these approaches. First, the loss function is not efficient for classification. Second, there is unbalance problem between the past and new classes. Third, the size of pre-decided exemplars is usually limited and they might not be distinguishable from unseen new classes. Forth, the exemplars may not be allowed to be kept for a long time due to privacy regulations. To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation. We believe that the data generated by GANs have much less privacy issues than real images because GANs do not directly copy any real image patches. We evaluate the proposed method on CIFAR-100, Flower-102, and MS-Celeb-1M-Base datasets and extensive experiments demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (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"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "deesil deep shallow incremental learning"
    - "end to end incremental learning"

Connected through hyperedge HE-2 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "the limits and potentials of deep learning for robotics"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-3 (induced by source paper "lifelong learning with searchable extension units"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "multinomial distribution learning for effective neural architecture search"
    - "deep learning for classical japanese literature"
    - "reinforced continual learning"
    - "darts differentiable architecture search"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "knowledge distillation for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "recent progress in semantic image segmentation"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "similarity preserving knowledge distillation"
    - "learning without memorizing"
    - "tree cnn a deep convolutional neural network for lifelong learning"

Connected through hyperedge HE-6 (induced by source paper "compacting picking and growing for unforgetting continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "task free continual learning"
    - "houdini lifelong learning as program synthesis"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"

Connected through hyperedge HE-7 (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"
    - "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"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-8 (induced by source paper "incremental learning techniques for semantic segmentation"):
  with members:
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "learning without memorizing"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "incremental training of deep convolutional neural networks"
    - "game theoretic analysis of road user safety scenarios involving autonomous vehicles"

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)