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: "optimal iris fuzzy sketches"
  Abstract: Fuzzy sketches, introduced as a link between biometry and cryptography, are a way of handling biometric data matching as an error correction issue. We focus here on iris biometrics and look for the best error-correcting code in that respect. We show that two-dimensional iterative min-sum decoding leads to results near the theoretical limits. In particular, we experiment our techniques on the iris challenge evaluation (ICE) database and validate our findings.

Connected through hyperedge HE-1 (induced by source paper "neural fuzzy extractors a secure way to use artificial neural networks for biometric user authentication"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "in defense of the triplet loss for person re identification"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "deep image retrieval learning global representations for image search"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "ensuring patients privacy in a cryptographic based electronic health records using bio cryptography"):
  with members:
    - "a criticism of the current security privacy and accountability issues in electronic health records"

Connected through hyperedge HE-3 (induced by source paper "thrive threshold homomorphic encryption based secure and privacy preserving biometric verification system"):
  with members:
    - "identification with encrypted biometric data"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-4 (induced by source paper "on fuzzy syndrome hashing with ldpc coding"):
  with members:
    - "on the decoding complexity of cyclic codes up to the bch bound"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"
    - "coding solutions for the secure biometric storage problem"

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)