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 privacy preserving visual recognition via adversarial training a pilot study"
  Abstract: This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework. The proposed framework explicitly learns a degradation transform for the original video inputs, in order to optimize the trade-off between target task performance and the associated privacy budgets on the degraded video. A notable challenge is that the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because a strong protection of privacy has to sustain against any possible model that tries to hack privacy information. Such an uncommon situation has motivated us to propose two strategies, i.e., budget model restarting and ensemble, to enhance the generalization of the learned degradation on protecting privacy against unseen hacker models. Novel training strategies, evaluation protocols, and result visualization methods have been designed accordingly. Two experiments on privacy-preserving action recognition, with privacy budgets defined in various ways, manifest the compelling effectiveness of the proposed framework in simultaneously maintaining high target task (action recognition) performance while suppressing the privacy breach risk.

Connected through hyperedge HE-1 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition"):
  with members:
    - "privacy preserving image based localization"
    - "xonn xnor based oblivious deep neural network inference"
    - "low latency privacy preserving inference"
    - "gazelle a low latency framework for secure neural network inference"
    - "stealing hyperparameters in machine learning"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "deepsecure scalable provably secure deep learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "an adversarial learning framework for preserving users anonymity in face based emotion recognition"):
  with members:
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "minimax filter learning to preserve privacy from inference attacks"

Connected through hyperedge HE-3 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dada deep adversarial data augmentation for extremely low data regime classification"

Connected through hyperedge HE-4 (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"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a hybrid model for identity obfuscation by face replacement"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-5 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "generative adversarial privacy"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial learning of privacy preserving and task oriented representations"):
  with members:
    - "learning privacy preserving encodings through adversarial training"
    - "knockoff nets stealing functionality of black box models"
    - "learning to anonymize faces for privacy preserving action detection"
    - "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-7 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"):
  with members:
    - "k beam minimax efficient optimization for deep adversarial learning"
    - "image to image translation for cross domain disentanglement"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "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-8 (induced by source paper "adversarial task specific privacy preservation under attribute attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "learning adversarially fair and transferable representations"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards robust and privacy preserving text representations"
    - "privacy preserving distributed deep learning for clinical data"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

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)