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: "Efficient Approximation of Channel Capacities"
  Abstract: We propose an iterative method for approximately computing the capacity of discrete memoryless channels, possibly under additional constraints on the input distribution. Based on duality of convex programming, we derive explicit upper and lower bounds for the capacity. The presented method requires    $O(M^{2} N \sqrt {\log N}/\varepsilon )$    to provide an estimate of the capacity to within    $\varepsilon $   , where    $N$    and    $M$    denote the input and output alphabet size; a single iteration has a complexity    $O(M N)$   . We also show how to approximately compute the capacity of memoryless channels having a bounded continuous input alphabet and a countable output alphabet under some mild assumptions on the decay rate of the channel’s tail. It is shown that discrete-time Poisson channels fall into this problem class. As an example, we compute sharp upper and lower bounds for the capacity of a discrete-time Poisson channel with a peak-power input constraint.

Connected through hyperedge HE-1 (induced by source paper "Efficient Approximation of Quantum Channel Capacities"):
  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