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 "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.

Includes members:
  Member "a basic compositional model for spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating temporal information to spatial information in a neural circuit")
  Member "dictionary learning by dynamical neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spiking neural network with local learning rules derived from nonnegative similarity matching")
  Member "neuro ram unit with applications to similarity testing and compression in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "winner take all computation in spiking neural networks")
  Member "sparse coding by spiking neural networks convergence theory and computational results":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spiking neural network with local learning rules derived from nonnegative similarity matching")
  Member "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultra low energy charge trap flash based synapse enabled by parasitic leakage mitigation")
  Member "bio inspired unsupervised learning of visual features leads to robust invariant object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "an event based architecture for solving constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vertex coloring of graphs via phase dynamics of coupled oscillatory networks")
  Member "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware")

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)