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: "vlsi extreme learning machine a design space exploration"
  Abstract: In this paper, we describe a compact low-power, high performance hardware implementation of the extreme learning machine (ELM) for machine learning applications. Mismatch in current mirrors are used to perform the vector-matrix multiplication that forms the first stage of this classifier and is the most computationally intensive. Both regression and classification (on UCI data sets) are demonstrated and a design space tradeoff between speed, power and accuracy is explored. Our results indicate that for a wide set of problems, σVT in the range of 15−25mV gives optimal results. An input weight matrix rotation method to extend the input dimension and hidden layer size beyond the physical limits imposed by the chip is also described. This allows us to overcome a major limit imposed on most hardware machine learners. The chip is implemented in a 0.35μm CMOS process and occupies a die area of around 5 mm × 5 mm. Operating from a 1 V power supply, it achieves an energy efficiency of 0.47 pJ/MAC at a classification rate of 31.6 kHz.

Connected through hyperedge HE-1 (induced by source paper "a stacked autoencoder neural network based automated feature extraction method for anomaly detection in on line condition monitoring"):
  with members:
    - "deep learning using rectified linear units relu"
    - "on the construction of extreme learning machine for online and offline one class classification an expanded toolbox"
    - "hardware architecture for large parallel array of random feature extractors applied to image recognition"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "semi trained memristive crossbar computing engine with in situ learning accelerator"):
  with members:
    - "memory and information processing in neuromorphic systems"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "design and synthesis of ultralow energy spin memristor threshold logic"

Connected through hyperedge HE-3 (induced by source paper "an iot endpoint system on chip for secure and energy efficient near sensor analytics"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "yodann an architecture for ultra low power binary weight cnn acceleration"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "training deep neural networks with low precision multiplications"

Connected through hyperedge HE-4 (induced by source paper "hardware architecture for large parallel array of random feature extractors applied to image recognition"):
  with members:
    - "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
    - "learning spike time codes through morphological learning with binary synapses"
    - "a neuromorphic hardware framework based on population coding"
    - "going deeper with convolutions"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"
    - "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites"
    - "network in network"

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)