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: "coding solutions for the secure biometric storage problem"
  Abstract: The paper studies the problem of securely storing biometric passwords, such as fingerprints and irises. With the help of coding theory Juels and Wattenberg derived in 1999 a scheme where similar input strings will be accepted as the same biometric. In the same time nothing could be learned from the stored data. They called their scheme a "fuzzy commitment scheme". In this paper we will revisit the solution of Juels and Wattenberg and we will provide answers to two important questions: What type of error-correcting codes should be used and what happens if biometric templates are not uniformly distributed, i.e. the biometric data come with redundancy. Answering the first question will lead us to the search for low-rate large-minimum distance error-correcting codes which come with efficient decoding algorithms up to the designed distance. In order to answer the second question we relate the rate required with a quantity connected to the "entropy" of the string, trying to estimate a sort of "capacity", if we want to see a flavor of the converse of Shannon's noisy coding theorem. Finally we deal with side-problems arising in a practical implementation and we propose a possible solution to the main one that seems to have so far prevented real life applications of the fuzzy scheme, as far as we know.

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"
    - "on fuzzy syndrome hashing with ldpc coding"
    - "fast encoding and decoding of gabidulin codes"

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"
    - "on fuzzy syndrome hashing with ldpc coding"
    - "spread decoding in extension fields"
    - "spread codes and spread decoding in network coding"
    - "coding for errors and erasures in random network coding"

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"
    - "on fuzzy syndrome hashing with ldpc coding"
    - "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"
    - "optimal iris fuzzy sketches"

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)