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: "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
  Abstract: This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases. Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding, which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible. We then define the coding gain for ANN search using information-theoretic measures. We next show that the classical approach to this problem, which consists of binarization of the projected vectors is sub-optimal. Instead, a properly designed ternary encoding achieves higher coding gains and lower complexity.

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"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "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"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "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"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-4 (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"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "neural codes for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving identification using sparse approximation with ambiguization"):
  with members:
    - "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.IT(Information Theory)