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: "plug play generative networks conditional iterative generation of images in latent space"
  Abstract: Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. (2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models "Plug and Play Generative Networks". PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network). Our method also improves the state of the art of Multifaceted Feature Visualization, which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.

Connected through hyperedge HE-1 (induced by source paper "opengan open set generative adversarial networks"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "the importance of metric learning for robotic vision open set recognition and active learning"
    - "cgans with projection discriminator"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "spectral normalization for generative adversarial networks"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "focl feature oriented continual learning for generative models"):
  with members:
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "generative models from the perspective of continual learning"
    - "continual learning in generative adversarial nets"
    - "modulating early visual processing by language"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-3 (induced by source paper "shallow encoder deep decoder sedd networks for image encryption and decryption"):
  with members:
    - "deep models under the gan information leakage from collaborative deep learning"
    - "full resolution image compression with recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-5 (induced by source paper "pixel level dense prediction without decoder"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "dreaming to distill data free knowledge transfer via deepinversion"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "relational knowledge distillation"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "variational information distillation for knowledge transfer"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "how good is my gan"

Connected through hyperedge HE-7 (induced by source paper "the six fronts of the generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-8 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

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)