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: "bingan learning compact binary descriptors with a regularized gan"
  Abstract: In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors). We employ the dimensionality reduction that takes place in the intermediate layers of the discriminator network and train binarized low-dimensional representation of the penultimate layer to mimic the distribution of the higher-dimensional preceding layers. To achieve this, we introduce two loss terms that aim at: (i) reducing the correlation between the dimensions of the binarized low-dimensional representation of the penultimate layer i. e. maximizing joint entropy) and (ii) propagating the relations between the dimensions in the high-dimensional space to the low-dimensional space. We evaluate the resulting binary image descriptors on two challenging applications, image matching and retrieval, and achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "auto encoding twin bottleneck hashing"):
  with members:
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "embarrassingly simple binary representation learning"
    - "zero shot sketch image hashing"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "semi supervised classification with graph convolutional networks"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

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 "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-4 (induced by source paper "embarrassingly simple binary representation learning"):
  with members:
    - "zero shot sketch image hashing"
    - "subic a supervised structured binary code for image search"
    - "mihash online hashing with mutual information"
    - "binary generative adversarial networks for image retrieval"
    - "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-5 (induced by source paper "adversarial autoencoders for compact representations of 3d point clouds"):
  with members:
    - "pointwise an unsupervised point wise feature learning network"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "graph based over segmentation methods for 3d point clouds"
    - "training triplet networks with gan"
    - "semantic visual localization"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning with bidirectional gans"):
  with members:
    - "training triplet networks with gan"
    - "fast training of triplet based deep binary embedding networks"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "deep metric learning using triplet network"

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)