human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a 128 channel extreme learning machine based neural decoder for brain machine interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "precise neural network computation with imprecise analog devices")
  Member "a neuromorphic hardware framework based on population coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware")
  Member "online and adaptive pseudoinverse solutions for elm weights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adepos a novel approximate computing framework for anomaly detection systems and its implementation in 65nm cmos")
  Member "random projections for k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved subsampled randomized hadamard transform for linear svm")

Question: Please tell me which class the source paper 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)