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: "adversarial learning with local coordinate coding"
  Abstract: Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent distribution learned from data, which, however, is hard to be used for sampling in GANs. In this paper, rather than sampling from the pre-defined prior distribution, we propose a Local Coordinate Coding (LCC) based sampling method to improve GANs. We derive a generalization bound for LCC based GANs and prove that a small dimensional input is sufficient to achieve good generalization. Extensive experiments on various real-world datasets demonstrate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "hierarchical neural architecture search for single image super resolution"):
  with members:
    - "discrimination aware network pruning for deep model compression"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "lightweight feature fusion network for single image super resolution"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"

Connected through hyperedge HE-3 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-4 (induced by source paper "sim real joint reinforcement transfer for 3d indoor navigation"):
  with members:
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "video to video synthesis"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "chalet cornell house agent learning environment"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "dm gan dynamic memory generative adversarial networks for text to image synthesis"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "spectral normalization for generative adversarial networks"
    - "text to image synthesis via symmetrical distillation networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "reference network for neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "improving the transformer translation model with document level context"
    - "document level neural machine translation with hierarchical attention networks"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "multi marginal wasserstein gan"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "pair wise exchangeable feature extraction for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "attention aware multi stroke style transfer"
    - "etnet error transition network for arbitrary style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "style separation and synthesis via generative adversarial networks"

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)