human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "class specific differential detection in diffractive optical neural networks improves inference accuracy"
  Abstract: Optical computing provides unique opportunities in terms of parallelization, scalability, power efficiency, and computational speed and has attracted major interest for machine learning. Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference, achieving promising performance for object classification and imaging. We demonstrate systematic improvements in diffractive optical neural networks, based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity. In this differential detection scheme, each class is assigned to a separate pair of detectors, behind a diffractive optical network, and the class inference is made by maximizing the normalized signal difference between the photodetector pairs. Using this differential detection scheme, involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons, we numerically achieved blind testing accuracies of 98.54%, 90.54%, and 48.51% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively. Moreover, by utilizing the inherent parallelization capability of optical systems, we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel. We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks. Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel, our simulations achieved blind testing accuracies of 98.52%, 91.48%, and 50.82% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively, coming close to the performance of some of the earlier generations of all-electronic deep neural networks, e.g., LeNet, which achieves classification accuracies of 98.77%, 90.27%, and 55.21% corresponding to the same datasets, respectively. In addition to these jointly optimized diffractive neural networks, we also independently optimized multiple diffractive networks and utilized them in a way that is similar to ensemble methods practiced in machine learning; using 3 independently optimized differential diffractive neural networks that optically project their light onto a common output/detector plane, we numerically achieved blind testing accuracies of 98.59%, 91.06%, and 51.44% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively. Through these systematic advances in designing diffractive neural networks, the reported classification accuracies set the state of the art for all-optical neural network design. The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific.

Connected through hyperedge HE-1 (induced by source paper "express wavenet a low parameter optical neural network with random shift wavelet pattern"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "an optical diffractive deep neural network with multiple frequency channels"
    - "a parallel optical image security system with cascaded phase only masks"
    - "optical machine learning with incoherent light and a single pixel detector"
    - "all optical machine learning using diffractive deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "multiple image encryption and hiding with an optical diffractive neural network"):
  with members:
    - "review on optical image hiding and watermarking techniques"
    - "all optical machine learning using diffractive deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "all optical machine learning using diffractive deep neural networks"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "principles of neuromorphic photonics"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.NE(Neural and Evolutionary Computing)