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: "2013 special issue learning the pseudoinverse solution to network weights"
  Abstract: The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the ''learning method''-computation of the pseudoinverse by singular value decomposition-is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse precisely, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition.

Connected through hyperedge HE-1 (induced by source paper "adepos a novel approximate computing framework for anomaly detection systems and its implementation in 65nm cmos"):
  with members:
    - "a stacked autoencoder neural network based automated feature extraction method for anomaly detection in on line condition monitoring"
    - "neuron pruning for compressing deep networks using maxout architectures"
    - "adam a method for stochastic optimization"
    - "one class classification taxonomy of study and review of techniques"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-2 (induced by source paper "enhanced image classification with a fast learning shallow convolutional neural network"):
  with members:
    - "deep learning in neural networks"
    - "explicit computation of input weights in extreme learning machines"
    - "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
    - "constrained extreme learning machines a study on classification cases"
    - "convolutional kernel networks"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-3 (induced by source paper "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"):
  with members:
    - "explicit computation of input weights in extreme learning machines"
    - "constrained extreme learning machines a study on classification cases"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "multi column deep neural networks for image classification"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-4 (induced by source paper "an online learning algorithm for neuromorphic hardware implementation"):
  with members:
    - "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"
    - "a neuromorphic hardware framework based on population coding"
    - "fpga implementation of the car model of the cochlea"

Connected through hyperedge HE-5 (induced by source paper "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"):
  with members:
    - "fpga implementation of the car model of the cochlea"

Connected through hyperedge HE-6 (induced by source paper "a trainable neuromorphic integrated circuit that exploits device mismatch"):
  with members:
    - "a neuromorphic hardware framework based on population coding"

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)