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: "connecting pixels to privacy and utility automatic redaction of private information in images"
  Abstract: Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual. By conducting a user study we find that obfuscating the image regions related to the private information leads to privacy while retaining utility of the images. Moreover, by varying the size of the regions different privacy-utility trade-offs can be achieved. Our findings argue for a "redaction by segmentation" paradigm. Hence, we propose the first sizable dataset of private images "in the wild" annotated with pixel and instance level labels across a broad range of privacy classes. We present the first model for automatic redaction of diverse private information.

Connected through hyperedge HE-1 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "graph embedding techniques applications and performance a survey"
    - "a hybrid model for identity obfuscation by face replacement"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "walk2friends inferring social links from mobility profiles"

Connected through hyperedge HE-2 (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-3 (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"
    - "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-4 (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"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "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"
    - "learning differentially private recurrent language models"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"

Connected through hyperedge HE-5 (induced by source paper "knockoff nets stealing functionality of black box models"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exploring the space of black box attacks on deep neural networks"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "deep mutual learning"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-6 (induced by source paper "adversarial scene editing automatic object removal from weak supervision"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "generative image inpainting with contextual attention"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "siamese generative adversarial privatizer for biometric data"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "adversarial image perturbation for privacy protection a game theory perspective"

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)