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: "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
  Abstract: Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35-  $\mu$  m CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by   $\approx 2X$  .

Connected through hyperedge HE-1 (induced by source paper "precise neural network computation with imprecise analog devices"):
  with members:
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "memory and information processing in neuromorphic systems"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "adam a method for stochastic optimization"
    - "low precision arithmetic for deep learning"

Connected through hyperedge HE-2 (induced by source paper "is my neural network neuromorphic taxonomy recent trends and future directions in neuromorphic engineering"):
  with members:
    - "spiking neural network based region proposal networks for neuromorphic vision sensors"
    - "ebbiot a low complexity tracking algorithm for surveillance in iovt using stationary neuromorphic vision sensors"
    - "real time closed loop neural decoding on a neuromorphic chip"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "memory and information processing in neuromorphic systems"
    - "imagenet large scale visual recognition challenge"

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"
    - "vlsi extreme learning machine a design space exploration"
    - "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 "vlsi extreme learning machine a design space exploration"):
  with members:
    - "a neuromorphic hardware framework based on population coding"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "random projections for k means clustering"

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)