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: "face attribute prediction using off the shelf cnn features"
  Abstract: Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial descriptor construction, and attribute classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.

Connected through hyperedge HE-1 (induced by source paper "attribute guided coupled gan for cross resolution face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "style and content disentanglement in generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "the missing data encoder cross channel image completion with hide and seek adversarial network"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "generative face completion"
    - "colorization as a proxy task for visual understanding"
    - "beyond shared hierarchies deep multitask learning through soft layer ordering"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "multiple face analyses through adversarial learning"):
  with members:
    - "facial landmark detection a literature survey"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "improving landmark localization with semi supervised learning"
    - "adversarial multi task learning for text classification"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-4 (induced by source paper "feature level fusion from facial attributes for face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "label propagation for deep semi supervised learning"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "restoring highly corrupted images by impulse noise using radial basis functions interpolation"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "attribute guided deep polarimetric thermal to visible face recognition"):
  with members:
    - "toward marker free 3d pose estimation in lifting a deep multi view solution"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi task cnn model for attribute prediction"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "deep learning in information security"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "independently controllable factors"
    - "magnet a two pronged defense against adversarial examples"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "a secure mobile authentication alternative to biometrics"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-7 (induced by source paper "a deep face identification network enhanced by facial attributes prediction"):
  with members:
    - "multibiometric secure system based on deep learning"
    - "improving facial attribute prediction using semantic segmentation"
    - "an all in one convolutional neural network for face analysis"
    - "yfcc100m the new data in multimedia research"
    - "a lightened cnn for deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "the new data and new challenges in multimedia research"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "a face recognition signature combining patch based features with soft facial attributes"):
  with members:
    - "improving facial attribute prediction using semantic segmentation"
    - "do we really need to collect millions of faces for effective face recognition"
    - "identity mappings in deep residual networks"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"

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)