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: "binary generative adversarial networks for image retrieval"
  Abstract: The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way. By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one. In the proposed framework, we address two main problems: 1) how to directly generate binary codes without relaxation? 2) how to equip the binary representation with the ability of accurate image retrieval? We resolve these problems by proposing new sign-activation strategy and a loss function steering the learning process, which consists of new models for adversarial loss, a content loss, and a neighborhood structure loss. Experimental results on standard datasets (CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly outperforms existing hashing methods by up to 107\% in terms of~mAP (See Table tab.res.map.comp) Our anonymous code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "region wise generative adversarial imageinpainting for large missing areas"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "generative adversarial networks recent developments"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "the gan landscape losses architectures regularization and normalization"
    - "improving gan training via binarized representation entropy bre regularization"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "large scale gan training for high fidelity natural image synthesis"
    - "point cloud gan"

Connected through hyperedge HE-3 (induced by source paper "embarrassingly simple binary representation learning"):
  with members:
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "subic a supervised structured binary code for image search"
    - "mihash online hashing with mutual information"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-4 (induced by source paper "online hashing with efficient updating of binary codes"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "mihash online hashing with mutual information"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"
    - "the power of asymmetry in binary hashing"

Connected through hyperedge HE-5 (induced by source paper "pbgen partial binarization of deconvolution based generators for edge intelligence"):
  with members:
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "pca driven hybrid network design for enabling intelligence at the edge"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"

Connected through hyperedge HE-6 (induced by source paper "adversarial learning for fine grained image search"):
  with members:
    - "object part attention model for fine grained image classification"
    - "deep semantic hashing with generative adversarial networks"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "one shot fine grained instance retrieval"
    - "convolutional neural network architecture for geometric matching"
    - "visual search at ebay"
    - "unsupervised image to image translation networks"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-7 (induced by source paper "excavate condition invariant space by intrinsic encoder"):
  with members:
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep learning features at scale for visual place recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "addressing challenging place recognition tasks using generative adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-8 (induced by source paper "adversarial binary coding for efficient person re identification"):
  with members:
    - "adversarial metric attack for person re identification"
    - "deep semantic hashing with generative adversarial networks"
    - "features for multi target multi camera tracking and re identification"
    - "hard aware point to set deep metric for person re identification"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "hashnet deep learning to hash by continuation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)