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: "secure biometrics concepts authentication architectures and challenges"
  Abstract: Biometrics are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords and are not easily lost or forged like identifying documents. At the same time, biometrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article, we present an overview of secure biometrics, also referred to as biometric template protection, an emerging class of methods that address these concerns.

Connected through hyperedge HE-1 (induced by source paper "controllable identifier measurements for private authentication with secret keys"):
  with members:
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "privacy secrecy and storage with noisy identifiers"
    - "secret key based authentication with a privacy constraint"
    - "converses for secret key agreement and secure computing"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-2 (induced by source paper "inference based similarity search in randomized montgomery domains for privacy preserving biometric identification"):
  with members:
    - "statistical meta analysis of presentation attacks for secure multibiometric systems"
    - "learning robust deep face representation"
    - "learning face representation from scratch"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-3 (induced by source paper "multibiometric secure system based on deep learning"):
  with members:
    - "the nd iris 0405 iris image dataset"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-4 (induced by source paper "passbio privacy preserving user centric biometric authentication"):
  with members:
    - "secure fingerprint alignment and matching protocols"
    - "expsos secure and verifiable outsourcing of exponentiation operations for mobile cloud computing"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"

Connected through hyperedge HE-5 (induced by source paper "secret key based identification and authentication with a privacy constraint"):
  with members:
    - "secret key based authentication with a privacy constraint"
    - "lossy source coding with reconstruction privacy"
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"
    - "authentication with distortion criteria"

Connected through hyperedge HE-6 (induced by source paper "secret key based authentication with a privacy constraint"):
  with members:
    - "lossy source coding with reconstruction privacy"
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "authentication with distortion criteria"

Connected through hyperedge HE-7 (induced by source paper "authentication with a guessing adversary"):
  with members:
    - "authentication with distortion criteria"

Connected through hyperedge HE-8 (induced by source paper "polar coding for secret key generation"):
  with members:
    - "achieving secrecy capacity of the wiretap channel and broadcast channel with a confidential component"
    - "polar coding for the broadcast channel with confidential messages and constrained randomization"
    - "universal source polarization and an application to a multi user problem"
    - "efficient one way secret key agreement and private channel coding via polarization"
    - "separation of reliability and secrecy in rate limited secret key generation"
    - "strong coordination with polar codes"
    - "randomness and dependencies extraction via polarization with applications to slepian wolf coding and secrecy"
    - "achieving the secrecy capacity of wiretap channels using polar codes"

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.CR(Cryptography and Security)