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 "reconstruction of privacy sensitive data from protected templates"):
  Abstract: In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.

Includes members:
  Member "on the reconstruction of face images from deep face templates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel classification selection approach for the self updating of template based face recognition systems")
  Member "learning to compress and search visual data in large scale systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups")
  Member "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "single component privacy guarantees in helper data systems and sparse coding with ambiguation")
  Member "sparse ternary codes for similarity search have higher coding gain than dense binary codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "single component privacy guarantees in helper data systems and sparse coding with ambiguation")
  Member "privacy preserving identification using sparse approximation with ambiguization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups")
  Member "inverting visual representations with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable cnns for object classification")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)