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: "pt spike a precise time dependent single spike neuromorphic architecture with efficient supervised learning"
  Abstract: One of the most exciting advancements in AI over the last decade is the wide adoption of ANNs, such as DNN and CNN, in many real-world applications. However, the underlying massive amounts of computation and storage requirement greatly challenge their applicability in resource-limited platforms like the drone, mobile phone, and IoT devices etc. The third generation of neural network model--Spiking Neural Network (SNN), inspired by the working mechanism and efficiency of human brain, has emerged as a promising solution for achieving more impressive computing and power efficiency within light-weighted devices (e.g. single chip). However, the relevant research activities have been narrowly carried out on conventional rate-based spiking system designs for fulfilling the practical cognitive tasks, underestimating SNN's energy efficiency, throughput, and system flexibility. Although the time-based SNN can be more attractive conceptually, its potentials are not unleashed in realistic applications due to lack of efficient coding and practical learning schemes. In this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture, namely "PT-Spike", is developed to bridge this gap. Three constituent hardware-favorable techniques: precise single-spike temporal encoding, efficient supervised temporal learning, and fast asymmetric decoding are proposed accordingly to boost the energy efficiency and data processing capability of the time-based SNN at a more compact neural network model size when executing real cognitive tasks. Simulation results show that "PT-Spike" demonstrates significant improvements in network size, processing efficiency and power consumption with marginal classification accuracy degradation when compared with the rate-based SNN and ANN under the similar network configuration.

Connected through hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "red a reram based deconvolution accelerator"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "revisiting small batch training for deep neural networks"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-2 (induced by source paper "deepn jpeg a deep neural network favorable jpeg based image compression framework"):
  with members:
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "simulated annealing for jpeg quantization"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)