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: "on fuzzy syndrome hashing with ldpc coding"
  Abstract: The last decades have seen a growing interest in hash functions that allow some sort of tolerance, e.g. for the purpose of biometric authentication. Among these, the syndrome fuzzy hashing construction allows to securely store biometric data and to perform user authentication without the need of sharing any secret key. This paper analyzes this model, showing that it offers a suitable protection against information leakage and several advantages with respect to similar solutions, such as the fuzzy commitment scheme. Furthermore, the design and characterization of LDPC codes to be used for this purpose is addressed.

Connected through hyperedge HE-1 (induced by source paper "fuzzy authentication using rank distance"):
  with members:
    - "on the genericity of maximum rank distance and gabidulin codes"
    - "new criteria for mrd and gabidulin codes and some rank metric code constructions"
    - "algebraic structures of mrd codes"
    - "on burst error correction and storage security of noisy data"
    - "subspace fuzzy vault"
    - "fast encoding and decoding of gabidulin codes"
    - "coding solutions for the secure biometric storage problem"

Connected through hyperedge HE-2 (induced by source paper "subspace fuzzy vault"):
  with members:
    - "on burst error correction and storage security of noisy data"
    - "an algebraic approach for decoding spread codes"
    - "spread decoding in extension fields"
    - "spread codes and spread decoding in network coding"
    - "coding for errors and erasures in random network coding"
    - "coding solutions for the secure biometric storage problem"

Connected through hyperedge HE-3 (induced by source paper "on burst error correction and storage security of noisy data"):
  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.IT(Information Theory)