human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  Abstract: Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light–matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. An optical machine learning framework termed Diffractive Deep Neural Networks has been expanded to compute a variety of desired tasks through broadband light and simultaneous processing of a wide and continuous range of wavelengths. Aydogan Ozcan and colleagues at the University of California, Los Angeles, USA, incorporated the wavelength-dependent nature of light–matter interaction occurring at multiple layers of a diffractive network to learn and extract input information at increasing levels of complexity. Simultaneously analyzing and processing light across many wavelengths present unique opportunities to enhance the inference and generalization capabilities of diffractive optical networks to perform machine learning tasks, e.g. all-optical object recognition, as well as to design deterministic and task-specific optical components, expanding the optical design space beyond human intuition.

Includes members:
  Member "analysis of diffractive optical neural networks and their integration with electronic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "express wavenet a low parameter optical neural network with random shift wavelet pattern")
  Member "class specific differential detection in diffractive optical neural networks improves inference accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "express wavenet a low parameter optical neural network with random shift wavelet pattern")
  Member "virtual histological staining of unlabelled tissue autofluorescence images via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan based virtual re staining a promising solution for whole slide image analysis")
  Member "all optical machine learning using diffractive deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation")
  Member "phase recovery and holographic image reconstruction using deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exascale deep learning for scientific inverse problems")
  Member "deep learning enhanced mobile phone microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning")
  Member "principles of neuromorphic photonics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of diffractive optical neural networks and their integration with electronic neural networks")
  Member "lensless computational imaging through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blending diverse physical priors with neural networks")

Question: Please tell me which class the source paper 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)