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 and adaptive pseudoinverse solutions for elm weights"
  Abstract: The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use. The solution of the hidden layer weights by means of a matrix pseudoinverse operation is a significant contributor to the utility of the method; however, the conventional calculation of the pseudoinverse by means of a singular value decomposition (SVD) is not always practical for large data sets or for online updates to the solution. In this paper we discuss incremental methods for solving the pseudoinverse which are suitable for ELM. We show that careful choice of methods allows us to optimize for accuracy, ease of computation, or adaptability of the solution.

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"
    - "an autoencoder approach to learning bilingual word representations"
    - "2013 special issue learning the pseudoinverse solution to network weights"

Connected through hyperedge HE-2 (induced by source paper "event based feature extraction using adaptive selection thresholds"):
  with members:
    - "invariant feature extraction from event based stimuli"
    - "training deep spiking neural networks using backpropagation"
    - "hfirst a temporal approach to object recognition"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "racing to learn statistical inference and learning in a single spiking neuron with adaptive kernels"

Connected through hyperedge HE-3 (induced by source paper "emnist an extension of mnist to handwritten letters"):
  with members:
    - "converting static image datasets to spiking neuromorphic datasets using saccades"

Connected through hyperedge HE-4 (induced by source paper "vlsi extreme learning machine a design space exploration"):
  with members:
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "a neuromorphic hardware framework based on population coding"
    - "random projections for k means clustering"

Connected through hyperedge HE-5 (induced by source paper "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"):
  with members:
    - "explicit computation of input weights in extreme learning machines"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-6 (induced by source paper "adaptive online sequential elm for concept drift tackling"):
  with members:
    - "learning under concept drift an overview"

Connected through hyperedge HE-7 (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"
    - "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-8 (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"
    - "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)