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 "precision energy throughput scaling of generic matrix multiplication and convolution kernels via linear projections"):
  Abstract: Generic matrix multiplication (GEMM) and one-dimensional convolution/cross-correlation (CONV) kernels often constitute the bulk of the compute- and memory-intensive processing within image/audio recognition and matching systems. We propose a novel method to scale the energy and processing throughput of GEMM and CONV kernels for such error-tolerant multimedia applications by adjusting the precision of computation. Our technique employs linear projections to the input matrix or signal data during the top-level GEMM and CONV blocking and reordering. The GEMM and CONV kernel processing then uses the projected inputs and the results are accumulated to form the final outputs. Throughput and energy scaling takes place by changing the number of projections computed by each kernel, which in turn produces approximate results, i.e. changes the precision of the performed computation. Results derived from a voltage- and frequency-scaled ARM Cortex A15 processor running face recognition and music matching algorithms demonstrate that the proposed approach allows for 280%~440% increase of processing throughput and 75%~80% decrease of energy consumption against optimized GEMM and CONV kernels without any impact in the obtained recognition or matching accuracy. Even higher gains can be obtained if one is willing to tolerate some reduction in the accuracy of the recognition and matching applications.

Includes members:
  Member "throughput distortion computation of generic matrix multiplication toward a computation channel for digital signal processing systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "query processing for the internet of things coupling of device energy consumption and cloud infrastructure billing")
  Member "throughput scaling of convolution for error tolerant multimedia applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reliable linear sesquilinear and bijective operations on integer data streams via numerical entanglement")

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.MM(Multimedia)