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: "On Calibration of Modern Neural Networks."
  Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.

Connected through hyperedge HE-1 (induced by source paper "Densely Connected Convolutional Networks"):
  with members:
    - "Deeply-Supervised Nets"
    - "Deep Sparse Rectifier Neural Networks"
    - "Maxout Networks"
    - "Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation."
    - "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology"
    - "Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds"
    - "How to train your MAML."

Connected through hyperedge HE-2 (induced by source paper "Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"):
  with members:
    - "The Stanford CoreNLP Natural Language Processing Toolkit"
    - "Opinion Mining and Sentiment Analysis"
    - "Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction"
    - "Relational inductive biases, deep learning, and graph networks."
    - "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN"
    - "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks."
    - "The Good, The Bad & The Ugly Features: A Meta-analysis on User Review About Food Journaling Apps"
    - "Learning Topic-Oriented Word Embedding for Query Classification"

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: Computer science