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 "a reconfigurable low power high throughput architecture for deep network training"):
  Abstract: General purpose computing systems are used for a large variety of applications. Extensive supports for flexibility in these systems limit their energy efficiencies. Neural networks, including deep networks, are widely used for signal processing and pattern recognition applications. In this paper we propose a multicore architecture for deep neural network based processing. Memristor crossbars are utilized to provide low power high throughput execution of neural networks. The system has both training and recognition (evaluation of new input) capabilities. The proposed system could be used for classification, dimensionality reduction, feature extraction, and anomaly detection applications. The system level area and power benefits of the specialized architecture is compared with the NVIDIA Telsa K20 GPGPU. Our experimental evaluations show that the proposed architecture can provide up to five orders of magnitude more energy efficiency over GPGPUs for deep neural network processing.

Includes members:
  Member "training and operation of an integrated neuromorphic network based on metal oxide memristors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "building high level features using large scale unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")

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)