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 "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"):
  Abstract: Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task. Recently, it has been proposed that efficient stochastic solvers can be obtained via appropriately configured spiking neural networks performing Markov Chain Monte Carlo (MCMC) sampling. The possibility to run such models on massively parallel, low-power, neuromorphic hardware holds great promise; however, previously proposed networks are based on probabilistic spiking neurons, and thus rely on random number generators or external sources of noise to achieve the necessary stochasticity, leading to significant overhead in the implementation. Here we show how stochasticity can be achieved by implementing deterministic models of integrate and fire neurons using subthreshold analog circuits that are affected by thermal noise. We present an efficient implementation of spike-based CSP solvers implemented on a reconfigurable neural network VLSI device, which exploits the device's intrinsic noise sources. We apply the overall concept to the solution of generic Sudoku problems, and present experimental results obtained from the neuromorphic device generating solutions to the problem at high rates.

Includes members:
  Member "an event based architecture for solving constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the algorithmic power of spiking neural networks")
  Member "event driven contrastive divergence for spiking neuromorphic systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge")
  Member "neuromorphic electronic circuits for building autonomous cognitive systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "synaptic integration of spatiotemporal features with a dynamic neuromorphic processor")
  Member "a theoretical basis for efficient computations with noisy spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integrating temporal information to spatial information in a neural circuit")
  Member "the thermodynamic temperature of a rhythmic spiking network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "event driven contrastive divergence for spiking neuromorphic systems")

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)