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: "Oriented Response Networks"
  Abstract: Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce feature maps with location and orientation explicitly encoded. An ARF acts as a virtual filter bank containing the filter itself and its multiple unmaterialised rotated versions. During back-propagation, an ARF is collectively updated using errors from all its rotated versions. DCNNs using ARFs, referred to as Oriented Response Networks (ORNs), can produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks. The oriented response produced by ORNs can also be used for image and object orientation estimation tasks. Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we consistently observe that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance. We report the best results on several commonly used benchmarks.

Connected through hyperedge HE-1 (induced by source paper "Deeply-Supervised Nets"):
  with members:
    - "Deep multiple instance learning for image classification and auto-annotation"
    - "Maxout Networks"
    - "Densely Connected Convolutional Networks"
    - "DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model"

Connected through hyperedge HE-2 (induced by source paper "Do Capsule Networks Solve the Problem of Rotation Invariance for Traffic Sign Classification"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Adversarial Examples - A Complete Characterisation of the Phenomenon"):
  with members:
    - "Maxout Networks"

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