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 "a spiking neural network with local learning rules derived from nonnegative similarity matching"):
  Abstract: The design and analysis of spiking neural network algorithms will be accelerated by the advent of new theoretical approaches. In an attempt at such approach, we provide a principled derivation of a spiking algorithm for unsupervised learning, starting from the nonnegative similarity matching cost function. The resulting network consists of integrate-and-fire units and exhibits local learning rules, making it biologically plausible and also suitable for neuromorphic hardware. We show in simulations that the algorithm can perform sparse feature extraction and manifold learning, two tasks which can be formulated as nonnegative similarity matching problems.

Includes members:
  Member "dictionary learning by dynamical neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the algorithmic power of spiking neural networks")
  Member "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised learning by competing hidden units")
  Member "online representation learning with single and multi layer hebbian networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured and deep similarity matching via structured and deep hebbian networks")
  Member "a survey of neuromorphic computing and neural networks in hardware":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "sparse coding by spiking neural networks convergence theory and computational results":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integrating temporal information to spatial information in a neural circuit")
  Member "simple efficient and neural algorithms for sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "a network of spiking neurons for computing sparse representations in an energy efficient way":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "short term memory capacity in networks via the restricted isometry property")

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)