human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Deletion Codes in the High-Noise and High-Rate Regimes"
  Abstract: The noise model of deletions poses significant challenges in coding theory, with basic questions like the capacity of the binary deletion channel still being open. In this paper, we study the harder model of  worst case  deletions, with a focus on constructing efficiently decodable codes for the two extreme regimes of high-noise and high-rate. Specifically, we construct polynomial-time decodable codes with the following tradeoffs (for any    $ \varepsilon > 0$   ): 1) codes that can correct a fraction    $1- \varepsilon $    of deletions with rate    $ \mathop {\mathrm {poly}}\nolimits ( \varepsilon )$    over an alphabet of size    $ \mathop {\mathrm {poly}}\nolimits (1/ \varepsilon )$   ; 2) binary codes of rate    $1-\tilde {O}(\sqrt { \varepsilon })$    that can correct a fraction    $ \varepsilon $    of deletions; and 3) Binary codes that can be  list-decoded  from a fraction    $(1/2- \varepsilon )$    of deletions with rate    $ \mathop {\mathrm {poly}}\nolimits ( \varepsilon )$   . This paper gives the first efficient constructions which meet the qualitative goals of correcting a deletion fraction approaching 1 over bounded alphabets, and correcting a constant fraction of bit deletions with rate approaching 1 over a fixed alphabet. The above-mentioned results bring our understanding of deletion code constructions in these regimes to a similar level as worst case errors.

Connected through hyperedge HE-1 (induced by source paper "An improved bound on the fraction of correctable deletions"):
  with members:
    (none)

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics