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: "learning to anonymize faces for privacy preserving action detection"
  Abstract: There is an increasing concern in computer vision devices invading the privacy of their users by recording unwanted videos. On one hand, we want the camera systems/robots to recognize important events and assist human daily life by understanding its videos, but on the other hand we also want to ensure that they do not intrude people's privacy. In this paper, we propose a new principled approach for learning a video face anonymizer. We use an adversarial training setting in which two competing systems fight: (1) a video anonymizer that modifies the original video to remove privacy-sensitive information (i.e., human face) while still trying to maximize spatial action detection performance, and (2) a discriminator that tries to extract privacy-sensitive information from such anonymized videos. The end result is a video anonymizer that performs a pixel-level modification to anonymize each person's face, with minimal effect on action detection performance. We experimentally confirm the benefit of our approach compared to conventional hand-crafted video/face anonymization methods including masking, blurring, and noise adding. See the project page this https URL for a demo video and more results.

Connected through hyperedge HE-1 (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "the deepfake detection challenge dfdc preview dataset"
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "deepobfuscator adversarial training framework for privacy preserving image classification"
    - "privacy preserving image based localization"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "a hybrid model for identity obfuscation by face replacement"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "password conditioned anonymization and deanonymization with face identity transformers"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (induced by source paper "adversarial learning of privacy preserving and task oriented representations"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "knockoff nets stealing functionality of black box models"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "faceless person recognition privacy implications in social media"
    - "deep variational information bottleneck"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-4 (induced by source paper "training with the invisibles obfuscating images to share safely for learning visual recognition models"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "on classification of distorted images with deep convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-5 (induced by source paper "deepprivacy a generative adversarial network for face anonymization"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "dsfd dual shot face detector"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "s 3 fd single shot scale invariant face detector"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "what have we learned from deep representations for action recognition"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving image based localization"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "semantic visual localization"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "segmatch segment based place recognition in 3d point clouds"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "planet photo geolocation with convolutional neural networks"
    - "dsac differentiable ransac for camera localization"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "privacy aware offloading of deep neural networks"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "semi coupled two stream fusion convnets for action recognition at extremely low resolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)