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: "explicit computation of input weights in extreme learning machines"
  Abstract: We present a closed form expression for initializing the input weights in a multilayer perceptron, which can be used as the first step in synthesis of an Extreme Learning Machine. The expression is based on the standard function for a separating hyperplane as computed in multilayer perceptrons and linear Support Vector Machines; that is, as a linear combination of input data samples. In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer. The hidden layer weights are solved in the standard ELM fashion by computing the pseudoinverse of the hidden layer outputs and multiplying by the desired output values. All weights for this method can be computed in a single pass, and the resulting networks are more accurate and more consistent on some standard problems than regular ELM networks of the same size.

Connected through hyperedge HE-1 (induced by source paper "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"):
  with members:
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "online and adaptive pseudoinverse solutions for elm weights"

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"
    - "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"
    - "2013 special issue learning the pseudoinverse solution to network weights"
    - "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:
    - "constrained extreme learning machines a study on classification cases"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "2013 special issue learning the pseudoinverse solution to network 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"

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)