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: "towards a visual privacy advisor understanding and predicting privacy risks in images"
  Abstract: With an increasing number of users sharing information online, privacy implications entailing such actions are a major concern. For explicit content, such as user profile or GPS data, devices (e.g. mobile phones) as well as web services (e.g. Facebook) offer to set privacy settings in order to enforce the users' privacy preferences. We propose the first approach that extends this concept to image content in the spirit of a Visual Privacy Advisor. First, we categorize personal information in images into 68 image attributes and collect a dataset, which allows us to train models that predict such information directly from images. Second, we run a user study to understand the privacy preferences of different users w.r.t. such attributes. Third, we propose models that predict user specific privacy score from images in order to enforce the users' privacy preferences. Our model is trained to predict the user specific privacy risk and even outperforms the judgment of the users, who often fail to follow their own privacy preferences on image data.

Connected through hyperedge HE-1 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches"):
  with members:
    - "person recognition in personal photo collections"
    - "one pixel attack for fooling deep neural networks"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "sequential attacks on agents for long term adversarial goals"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "k beam minimax efficient optimization for deep adversarial learning"
    - "image to image translation for cross domain disentanglement"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

Connected through hyperedge HE-3 (induced by source paper "robust and undetectable white box watermarks for deep neural networks"):
  with members:
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "embedding watermarks into deep neural networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "image privacy prediction using deep neural networks"):
  with members:
    - "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"
    - "privacy prediction of images shared on social media sites using deep features"
    - "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-5 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "places an image database for deep scene understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "object scene convolutional neural networks for event recognition in images"
    - "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
    - "meta des"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "privaceye privacy preserving head mounted eye tracking using egocentric scene image and eye movement features"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "going deeper with convolutions"
    - "screenavoider protecting computer screens from ubiquitous cameras"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-7 (induced by source paper "understanding and controlling user linkability in decentralized learning"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "knock knock who s there membership inference on aggregate location data"
    - "a 4 nt author attribute anonymity by adversarial training of neural machine translation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "learning differentially private recurrent language models"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"

Connected through hyperedge HE-8 (induced by source paper "towards privacy preserving visual recognition via adversarial training a pilot study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "learning transferable architectures for scalable image recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

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)