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: "privacy prediction of images shared on social media sites using deep features"
  Abstract: Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online every day, the development of effective and efficient prediction methods for image privacy settings are highly needed. The performance of models critically depends on the choice of the feature representation. In this paper, we present an approach to image privacy prediction that uses deep features and deep image tags as feature representations. Specifically, we explore deep features at various neural network layers and use the top layer (probability) as an auto-annotation mechanism. The results of our experiments show that models trained on the proposed deep features and deep image tags substantially outperform baselines such as those based on SIFT and GIST as well as those that use "bag of tags" as features.

Connected through hyperedge HE-1 (induced by source paper "image privacy prediction using deep neural networks"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "unsupervised deep features for privacy image classification"):
  with members:
    - "tag based semantic features for scene image classification"
    - "indoor image representation by high level semantic features"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "speaking the same language matching machine to human captions by adversarial training"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "adversarial learning for neural dialogue generation"
    - "creativity generating diverse questions using variational autoencoders"
    - "face detection using deep learning an improved faster rcnn approach"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "image captioning with semantic attention"
    - "faceless person recognition privacy implications in social media"

Connected through hyperedge HE-4 (induced by source paper "connecting pixels to privacy and utility automatic redaction of private information in images"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "a domain based approach to social relation recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "face detection using deep learning an improved faster rcnn approach"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "exploiting saliency for object segmentation from image level labels"
    - "toward sensitive document release with privacy guarantees"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "towards a visual privacy advisor understanding and predicting privacy risks in images"):
  with members:
    - "privacy leakage through innocent content sharing in online social networks"
    - "universal adversarial perturbations"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep residual learning for image recognition"
    - "beyond frontal faces improving person recognition using multiple cues"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

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)