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: "The Poisson transform for unnormalised statistical models"
  Abstract: Contrary to standard statistical models, unnormalised statistical models only specify the likelihood function up to a constant. While such models are natural and popular, the lack of normalisation makes inference much more difficult. Extending classical results on the multinomial-Poisson transform (Baker In: J Royal Stat Soc 43(4):495---504, 1994), we show that inferring the parameters of a unnormalised model on a space $$\Omega $$Ω can be mapped onto an equivalent problem of estimating the intensity of a Poisson point process on $$\Omega $$Ω. The unnormalised statistical model now specifies an intensity function that does not need to be normalised. Effectively, the normalisation constant may now be inferred as just another parameter, at no loss of information. The result can be extended to cover non-IID models, which includes for example unnormalised models for sequences of graphs (dynamical graphs), or for sequences of binary vectors. As a consequence, we prove that unnormalised parameteric inference in non-IID models can be turned into a semi-parametric estimation problem. Moreover, we show that the noise-contrastive estimation method of Gutmann and Hyvarinen (J Mach Learn Res 13(1):307---361, 2012) can be understood as an approximation of the Poisson transform, and extended to non-IID settings. We use our results to fit spatial Markov chain models of eye movements, where the Poisson transform allows us to turn a highly non-standard model into vanilla semi-parametric logistic regression.

Connected through hyperedge HE-1 (induced by source paper "Variational Noise-Contrastive Estimation."):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey"):
  with members:
    - "Fields of Experts: a framework for learning image priors"
    - "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion"
    - "DeepNAT: Deep convolutional neural network for segmenting neuroanatomy"
    - "Detection and localization of spatially correlated point landmarks in medical images using an automatically learned conditional random field"

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