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 preserving visual learning using doubly permuted homomorphic encryption"
  Abstract: We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.

Connected through hyperedge HE-1 (induced by source paper "mechanism design for multi party machine learning"):
  with members:
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "federated learning strategies for improving communication efficiency"
    - "strategyproof linear regression in high dimensions"
    - "communication efficient learning of deep networks from decentralized data"
    - "truthful linear regression"
    - "auctions with severely bounded communication"

Connected through hyperedge HE-2 (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"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a hybrid model for identity obfuscation by face replacement"

Connected through hyperedge HE-3 (induced by source paper "password conditioned anonymization and deanonymization with face identity transformers"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "learning to anonymize faces for privacy preserving action detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving image based localization"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "learning to anonymize faces for privacy preserving action detection"
    - "semantic visual localization"
    - "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-5 (induced by source paper "learning to anonymize faces for privacy preserving action detection"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "learning latent super events to detect multiple activities in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (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"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-7 (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"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "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"
    - "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 "gradient leaks understanding and controlling deanonymization in federated learning"):
  with members:
    - "towards federated learning at scale system design"
    - "knockoff nets stealing functionality of black box models"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "sok security and privacy in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)