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: "fundamental limits of identification system with secret binding under noisy enrollment"
  Abstract: We investigate fundamental limits among identification, secrecy, template, and privacy-leakage rates in biometric identification system with secret binding from an information theoretic perspective. Ignatenko and Willems (2015) studied the system with estimation of individuals and clarified the fundamental limit among identification, secrecy, and privacy-leakage rates provided that the enrollment channel of the system is noiseless. In the enrollment process, however, it is highly considerable that noise occurs when bio-data is scanned. Recently, Gunlu and Kramer (2018) analyzed the system assuming that the channel is noisy when there is only one individual. In this paper, we analyze the model considering the estimation of individuals under noisy enrollment and characterize the capacity region of identification, secrecy, template, and privacy-leakage rates in the biometric identification system with secret binding. The obtained result shows that it reduces to the one given by Ignatenko and Willems (2015) as a special case where the enrollment channel is noiseless.

Connected through hyperedge HE-1 (induced by source paper "fundamental limits of biometric identification systems with strong secrecy"):
  with members:
    - "private authentication with physical identifiers through broadcast channel measurements"
    - "identification secrecy template and privacy leakage of biometric identification system under noisy enrollment"
    - "a new wiretap channel model and its strong secrecy capacity"
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "privacy secrecy and storage with multiple noisy measurements of identifiers"
    - "secret key based identification and authentication with a privacy constraint"
    - "achievability proof via output statistics of random binning"

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)