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 via layered sparse code design distributed servers and multiple access authorization"
  Abstract: We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the authorization level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.

Connected through hyperedge HE-1 (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"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "inverting visual representations with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-3 (induced by source paper "single component privacy guarantees in helper data systems and sparse coding"):
  with members:
    - "reconstruction of privacy sensitive data from protected templates"
    - "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"

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.IT(Information Theory)