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: "memory replay gans learning to generate images from new categories without forgetting"
  Abstract: Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (i.e. forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.

Connected through hyperedge HE-1 (induced by source paper "continual universal object detection"):
  with members:
    - "distilling object detectors with fine grained feature imitation"
    - "continual lifelong learning with neural networks a review"
    - "towards universal object detection by domain attention"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "learning without memorizing"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-2 (induced by source paper "lifelong gan continual learning for conditional image generation"):
  with members:
    - "model compression via distillation and quantization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "learning to generate long term future via hierarchical prediction"

Connected through hyperedge HE-3 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "oracle order robust adaptive continual learning"
    - "knowledge transfer in deep block modular neural networks"
    - "applied federated learning improving google keyboard query suggestions"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-4 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "marginal replay vs conditional replay for continual learning"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "unified probabilistic deep continual learning through generative replay and open set recognition"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep learning for classical japanese literature"
    - "reducing network agnostophobia"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-7 (induced by source paper "discorl continual reinforcement learning via policy distillation"):
  with members:
    - "continual learning for robotics"
    - "s trigger continual state representation learning via self triggered generative replay"
    - "continual reinforcement learning in 3d non stationary environments"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"

Connected through hyperedge HE-8 (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"
    - "revisiting distillation and incremental classifier learning"
    - "efficient lifelong learning with a gem"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory efficient experience replay for streaming learning"

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)