human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing"):
  Abstract: As deep networks are applied to an ever-expanding set of computer vision tasks, protecting general privacy in image data has become a critically important goal. This paper presents a new framework for privacy-preserving data sharing that is robust to adversarial attacks and overcomes the known issues existing in previous approaches. We introduce the concept of a Deep Poisoning Function (DPF), which is a module inserted into a pre-trained deep network designed to perform a specific vision task. The DPF is optimized to deliberately poison image data to prevent known adversarial attacks, while ensuring that the altered image data is functionally equivalent to the non-poisoned data for the original task. Given this equivalence, both poisoned and non-poisoned data can be used for further retraining or fine-tuning. Experimental results on image classification and face recognition tasks prove the efficacy of the proposed method.

Includes members:
  Member "privacy preserving deep visual recognition an adversarial learning framework and a new dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach")
  Member "the deepfake detection challenge dfdc preview dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "training with the invisibles obfuscating images to share safely for learning visual recognition models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepobfuscator adversarial training framework for privacy preserving image classification")
  Member "deepobfuscator adversarial training framework for privacy preserving image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "protecting gans against privacy attacks by preventing overfitting")
  Member "privacy preserving image based localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "towards privacy preserving visual recognition via adversarial training a pilot study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "learning to anonymize faces for privacy preserving action detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "password conditioned anonymization and deanonymization with face identity transformers")
  Member "a hybrid model for identity obfuscation by face replacement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")

Question: Please tell me which class the source paper 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)