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: "online representation learning with single and multi layer hebbian networks for image classification"
  Abstract: Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks.

Connected through hyperedge HE-1 (induced by source paper "structured and deep similarity matching via structured and deep hebbian networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "sparse deep predictive coding captures contour integration capabilities of the early visual system"
    - "meaningful representations emerge from sparse deep predictive coding"
    - "a spiking neural network with local learning rules derived from nonnegative similarity matching"
    - "deep sparse coding for invariant multimodal halle berry neurons"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "simple efficient and neural algorithms for sparse coding"
    - "difference target propagation"

Connected through hyperedge HE-2 (induced by source paper "local unsupervised learning for image analysis"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "dense associative memory is robust to adversarial inputs"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "adversarial feature learning"
    - "dense associative memory for pattern recognition"
    - "deep residual learning for image recognition"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-3 (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"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "simple efficient and neural algorithms for sparse coding"
    - "a network of spiking neurons for computing sparse representations in an energy efficient way"

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)