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: "toward fast neural computing using all photonic phase change spiking neurons"
  Abstract: The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain, namely, neurons and synapses. However, efforts have largely been invested in implementations in the electrical domain with potential limitations of switching speed, packing density of large integrated systems and interconnect losses. As an alternative, neuromorphic engineering in the photonic domain has recently gained attention. In this work, we propose a purely photonic operation of an Integrate-and-Fire Spiking neuron, based on the phase change dynamics of Ge2Sb2Te5 (GST) embedded on top of a microring resonator, which alleviates the energy constraints of PCMs in electrical domain. We also show that such a neuron can be potentially integrated with on-chip synapses into an all-Photonic Spiking Neural network inferencing framework which promises to be ultrafast and can potentially offer a large operating bandwidth.

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"
    - "pcnna a photonic convolutional neural network accelerator"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "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"
    - "pcnna a photonic convolutional neural network accelerator"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "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"
    - "pcnna a photonic convolutional neural network accelerator"
    - "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 "a photonic in memory computing primitive for spiking neural networks using phase change materials"):
  with members:
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-5 (induced by source paper "fluxonic processing of photonic synapse events"):
  with members:
    - "the largest cognitive systems will be optoelectronic"
    - "circuit designs for superconducting optoelectronic loop neurons"
    - "superconducting optoelectronic circuits for neuromorphic computing"

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)