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: "on the algorithmic power of spiking neural networks"
  Abstract: Spiking Neural Networks (SNN) are mathematical models in neuroscience to describe the dynamics among a set of neurons which interact with each other by firing spike signals to each other. Interestingly, recent works observed that for an integrate-and-fire model, when configured appropriately (e.g., after the parameters are learned properly), the neurons' firing rate, i.e., converges to an optimal solution of Lasso and certain quadratic optimization problems. Thus, SNN can be viewed as a natural algorithm for solving such convex optimization problems. However, theoretical understanding of SNN algorithms remains limited. In particular, only the convergence result for the Lasso problem is known, but the bounds of the convergence rate remain unknown. Therefore, we do not know any explicit complexity bounds for SNN algorithms. #R##N#In this work, we investigate the algorithmic power of the integrate-and-fire SNN model after the parameters are properly learned/configured. In particular, we explore what algorithms SNN can implement. We start by formulating a clean discrete-time SNN model to facilitate the algorithmic study. We consider two SNN dynamics and obtain the following results. #R##N#* We first consider an arguably simplest SNN dynamics with a threshold spiking rule, which we call simple SNN. We show that simple SNN solves the least square problem for a matrix $A\in\mathbb{R}^{m\times n}$ and vector $\mathbf{b} \in \mathbb{R}^m$ with timestep complexity $O(\kappa n/\epsilon)$. #R##N#* For the under-determined case, we observe that simple SNN may solve the $\ell_1$ minimization problem using an interesting primal-dual algorithm, which solves the dual problem by a gradient-based algorithm while updates the primal solution along the way. We analyze a variant dynamics and use simulation to serve as partial evidence to support the conjecture.

Connected through hyperedge HE-1 (induced by source paper "exploring deep spiking neural networks for automated driving applications"):
  with members:
    - "deep learning in spiking neural networks"
    - "demonstrating advantages of neuromorphic computation a pilot study"
    - "object detection based on lidar temporal pulses using spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "spiking deep networks with lif neurons"
    - "fast r cnn"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "bifurcation spiking neural network"):
  with members:
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "emnist an extension of mnist to handwritten letters"
    - "multi scale convolutional neural networks for time series classification"
    - "training deep spiking neural networks using backpropagation"
    - "spiking deep networks with lif neurons"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "integrating temporal information to spatial information in a neural circuit"):
  with members:
    - "counting to ten with two fingers compressed counting with spiking neurons"
    - "a basic compositional model for spiking neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "a theoretical basis for efficient computations with noisy spiking neurons"

Connected through hyperedge HE-4 (induced by source paper "counting to ten with two fingers compressed counting with spiking neurons"):
  with members:
    - "integrating temporal information to spatial information in a neural circuit"
    - "a basic compositional model for spiking neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"

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)