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: "an alternative decoding method for gabidulin codes in characteristic zero"
  Abstract: Gabidulin codes, originally defined over finite fields, are an important class of rank metric codes with various applications. Recently, their definition was generalized to certain fields of characteristic zero and a Welch–Berlekamp like algorithm with complexity O(n) was given. We propose a new application of Gabidulin codes over infinite fields: low-rank matrix recovery. Also, an alternative decoding approach is presented based on a Gao type key equation, reducing the complexity to at least O(n). This method immediately connects the decoding problem to well-studied problems, which have been investigated in terms of coefficient growth and numerical stability.

Connected through hyperedge HE-1 (induced by source paper "reed solomon codes over fields of characteristic zero"):
  with members:
    - "generalized gabidulin codes over fields of any characteristic"
    - "low rank matrix recovery using gabidulin codes in characteristic zero"
    - "guruswami sudan list decoding for complex reed solomon codes"
    - "syndrome decoding of reed solomon codes beyond half the minimum distance based on shift register synthesis"
    - "new list decoding algorithms for reed solomon and bch codes"

Connected through hyperedge HE-2 (induced by source paper "low rank matrix recovery using gabidulin codes in characteristic zero"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "row reduction applied to decoding of rank metric and subspace codes"):
  with members:
    - "low rank matrix recovery using gabidulin codes in characteristic zero"
    - "decoding interleaved gabidulin codes using alekhnovich s algorithm"
    - "sub quadratic decoding of one point hermitian codes"
    - "fast operations on linearized polynomials and their applications in coding theory"
    - "solving shift register problems over skew polynomial rings using module minimisation"
    - "power decoding reed solomon codes up to the johnson radius"
    - "explicit rank metric codes list decodable with optimal redundancy"
    - "generalised multi sequence shift register synthesis using module minimisation"

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)