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 identification using sparse approximation with ambiguization"
  Abstract: In this paper, we consider a privacy preserving encoding framework for identification applications covering biometrics, physical object security and the Internet of Things (IoT). The proposed framework is based on a sparsifying transform, which consists of a trained linear map, an element-wise nonlinearity, and privacy amplification. The sparsifying transform and privacy amplification are not symmetric for the data owner and data user. We demonstrate that the proposed approach is closely related to sparse ternary codes (STC), a recent information-theoretic concept proposed for fast approximate nearest neighbor (ANN) search in high dimensional feature spaces that being machine learning in nature also offers significant benefits in comparison to sparse approximation and binary embedding approaches. We demonstrate that the privacy of the database outsourced to a server as well as the privacy of the data user are preserved at a low computational cost, storage and communication burdens.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "generative adversarial networks for extreme learned image compression"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "context aware generative adversarial privacy"
    - "defeating image obfuscation with deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "group membership verification with privacy sparse or dense"):
  with members:
    - "privacy preserving group membership verification and identification"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-3 (induced by source paper "reconstruction of privacy sensitive data from protected templates"):
  with members:
    - "on the reconstruction of face images from deep face templates"
    - "learning to compress and search visual data in large scale systems"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "inverting visual representations with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "single component privacy guarantees in helper data systems and sparse coding with ambiguation"):
  with members:
    - "reconstruction of privacy sensitive data from protected templates"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"):
  with members:
    - "a multi layer network based on sparse ternary codes for universal vector compression"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "neural codes for image retrieval"

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.CR(Cryptography and Security)