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 "single component privacy guarantees in helper data systems and sparse coding with ambiguation"):
  Abstract: We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization. In particular, we focus on a privacy property that is often overlooked, namely how much leakage exists about one specific binary property of one component of the feature vector. This property is e.g. the sign or an indicator that a threshold is exceeded.  We provide evidence that both approaches are able to protect such sensitive binary variables, and discuss how system parameters need to be set.

Includes members:
  Member "reconstruction of privacy sensitive data from protected templates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "single component privacy guarantees in helper data systems and sparse coding")
  Member "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reconstruction of privacy sensitive data from protected templates")
  Member "sparse ternary codes for similarity search have higher coding gain than dense binary codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reconstruction of privacy sensitive data from protected templates")
  Member "privacy preserving identification using sparse approximation with ambiguization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups")
  Member "fuzzy extractors how to generate strong keys from biometrics and other noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a capacity achieving one message key agreement with finite blocklength analysis")

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.IT(Information Theory)