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: "training with the invisibles obfuscating images to share safely for learning visual recognition models"
  Abstract: High-performance visual recognition systems generally require a large collection of labeled images to train. The expensive data curation can be an obstacle for improving recognition performance. Sharing more data allows training for better models. But personal and private information in the data prevent such sharing. To promote sharing visual data for learning a recognition model, we propose to obfuscate the images so that humans are not able to recognize their detailed contents, while machines can still utilize them to train new models. We validate our approach by comprehensive experiments on three challenging visual recognition tasks; image classification, attribute classification, and facial landmark detection on several datasets including SVHN, CIFAR10, Pascal VOC 2012, CelebA, and MTFL. Our method successfully obfuscates the images from humans recognition, but a machine model trained with them performs within about 1% margin (up to 0.48%) of the performance of a model trained with the original, non-obfuscated data.

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"
    - "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"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "deepobfuscator adversarial training framework for privacy preserving image classification"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "better accuracy with quantified privacy representations learned via reconstructive adversarial network"
    - "privacy preserving deep inference for rich user data on the cloud"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "semantic image segmentation via deep parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-3 (induced by source paper "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"):
  with members:
    - "predictive inequity in object detection"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "exploring disentangled feature representation beyond face identification"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "what do deep networks like to see"
    - "a reductions approach to fair classification"
    - "learning gender neutral word embeddings"

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)