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: "sparse coding by spiking neural networks convergence theory and computational results"
  Abstract: In a spiking neural network (SNN), individual neurons operate autonomously and only communicate with other neurons sparingly and asynchronously via spike signals. These characteristics render a massively parallel hardware implementation of SNN a potentially powerful computer, albeit a non von Neumann one. But can one guarantee that a SNN computer solves some important problems reliably? In this paper, we formulate a mathematical model of one SNN that can be configured for a sparse coding problem for feature extraction. With a moderate but well-defined assumption, we prove that the SNN indeed solves sparse coding. To the best of our knowledge, this is the first rigorous result of this kind.

Connected through hyperedge HE-1 (induced by source paper "a spiking neural network with local learning rules derived from nonnegative similarity matching"):
  with members:
    - "dictionary learning by dynamical neural networks"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "simple efficient and neural algorithms for sparse coding"
    - "a network of spiking neurons for computing sparse representations in an energy efficient way"

Connected through hyperedge HE-2 (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"
    - "on the algorithmic power of 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"
    - "a theoretical basis for efficient computations with noisy spiking neurons"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate sparse coding of visual stimuli with a simple ultralow energy spiking architecture"):
  with members:
    - "densely connected convolutional networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "on the algorithmic power of spiking neural networks"):
  with members:
    - "a basic compositional model for spiking neural networks"
    - "dictionary learning by dynamical neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "an event based architecture for solving constraint satisfaction problems"
    - "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"
    - "learning precise spike train to spike train transformations in multilayer feedforward neuronal networks"

Connected through hyperedge HE-5 (induced by source paper "dictionary learning by dynamical neural networks"):
  with members:
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "how important is weight symmetry in backpropagation"
    - "sparse modeling for image and vision processing"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "pre synaptic pool modification pspm a supervised learning procedure for spiking neural networks"):
  with members:
    - "a spiking neural network with local learning rules derived from nonnegative similarity matching"
    - "supervised learning in spiking neural networks for precise temporal encoding"

Connected through hyperedge HE-7 (induced by source paper "local information with feedback perturbation suffices for dictionary learning in neural circuits"):
  with members:
    - "sparse modeling for image and vision processing"
    - "non negative matrix factorization with sparseness constraints"

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.LG(Machine Learning)