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: "pcnna a photonic convolutional neural network accelerator"
  Abstract: Convolutional Neural Networks (CNN) have been the centerpiece of many applications including but not limited to computer vision, speech processing, and Natural Language Processing (NLP). However, the computationally expensive convolution operations impose many challenges to the performance and scalability of CNNs. In parallel, photonic systems, which are traditionally employed for data communication, have enjoyed recent popularity for data processing due to their high bandwidth, low power consumption, and reconfigurability. Here we propose a Photonic Convolutional Neural Network Accelerator (PCNNA) as a proof of concept design to speedup the convolution operation for CNNs. Our design is based on the recently introduced silicon photonic microring weight banks, which use broadcast-and-weight protocol to perform Multiply And Accumulate (MAC) operation and move data through layers of a neural network. Here, we aim to exploit the synergy between the inherent parallelism of photonics in the form of Wavelength Division MUltiplexing (WDM) and sparsity of connections between input feature maps and kernels in CNNs. While our full system design offers up to more than 3 orders of magnitude speedup in execution time, its optical core potentially offer more than 5 order of magnitude speedup compared to state-of-the-art electronic counterparts.

Connected through hyperedge HE-1 (induced by source paper "analysis of diffractive optical neural networks and their integration with electronic neural networks"):
  with members:
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "principles of neuromorphic photonics"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a winograd based integrated photonics accelerator for convolutional neural networks"):
  with members:
    - "on chip optical convolutional neural networks"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "high freedom inverse design with deep neural network for metasurface filter in the visible"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"
    - "fully convolutional networks for semantic segmentation"

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.ET(Emerging Technologies)