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: "code constructions for physical unclonable functions and biometric secrecy systems"
  Abstract: The two-terminal key agreement problem with biometric or physical identifiers is considered. Two linear code constructions based on Wyner-Ziv coding are developed. The first construction uses random linear codes and achieves all points of the key-leakage-storage regions of the generated-secret and chosen-secret models. The second construction uses nested polar codes for vector quantization during enrollment and for error correction during reconstruction. Simulations show that the nested polar codes achieve privacy-leakage and storage rates that improve on existing code designs. One proposed code achieves a rate tuple that cannot be achieved by existing methods.

Connected through hyperedge HE-1 (induced by source paper "biometric and physical identifiers with correlated noise for controllable private authentication"):
  with members:
    - "private authentication with physical identifiers through broadcast channel measurements"
    - "controllable identifier measurements for private authentication with secret keys"
    - "secure and reliable key agreement with physical unclonable functions"
    - "coding for positive rate in the source model key agreement problem"
    - "privacy secrecy and storage with multiple noisy measurements of identifiers"
    - "converses for secret key agreement and secure computing"
    - "polar coding for secret key generation"
    - "secret key generation using correlated sources and channels"

Connected through hyperedge HE-2 (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"
    - "fundamental limits of identification system with secret binding under noisy enrollment"
    - "a new wiretap channel model and its strong secrecy capacity"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sign bit shaping using polar codes"):
  with members:
    - "probabilistically shaped multi level coding with polar codes for fading channels"
    - "efficient polar code construction for higher order modulation"
    - "polar codes for broadcast channels"
    - "llr based successive cancellation list decoding of polar codes"
    - "how to achieve the capacity of asymmetric channels"
    - "achievable rates for shaped bit metric decoding"
    - "list decoding of polar codes"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"

Connected through hyperedge HE-4 (induced by source paper "controllable identifier measurements for private authentication with secret keys"):
  with members:
    - "privacy secrecy and storage with noisy identifiers"
    - "secret key based authentication with a privacy constraint"
    - "converses for secret key agreement and secure computing"
    - "secure biometrics concepts authentication architectures and challenges"
    - "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)