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: "throughput distortion computation of generic matrix multiplication toward a computation channel for digital signal processing systems"
  Abstract: The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra libraries used in many computationally demanding digital signal processing (DSP) systems. We propose an acceleration technique for GEMM based on dynamically adjusting the imprecision (distortion) of computation. Our technique employs adaptive scalar companding and rounding to input matrix blocks followed by two forms of packing in floating-point that allow for concurrent calculation of multiple results. Since the adaptive companding process controls the increase of concurrency (via packing), the increase in processing throughput (and the corresponding increase in distortion) depends on the input data statistics. To demonstrate this, we derive the optimal throughput-distortion control framework for GEMM for the broad class of zero-mean, independent identically distributed, input sources. Our approach converts matrix multiplication in programmable processors into a computation channel: when increasing the processing throughput, the output noise (error) increases due to: (i) coarser quantization; and (ii) computational errors caused by exceeding the machine-precision limitations. We show that, under certain distortion in the GEMM computation, the proposed framework can significantly surpass 100% of the peak performance of a given processor. The practical benefits of our proposal are shown in a face recognition system and a multilayer perceptron system trained for metadata learning from a large music feature database.

Connected through hyperedge HE-1 (induced by source paper "query processing for the internet of things coupling of device energy consumption and cloud infrastructure billing"):
  with members:
    - "cloud based augmentation for mobile devices motivation taxonomies and open challenges"
    - "energy consumption of visual sensor networks impact of spatio temporal coverage"
    - "distributed time frequency division multiple access protocol for wireless sensor networks"
    - "internet of things iot a vision architectural elements and future directions"
    - "optimum transmission policies for battery limited energy harvesting nodes"
    - "optimal packet scheduling in an energy harvesting communication system"

Connected through hyperedge HE-2 (induced by source paper "reliable linear sesquilinear and bijective operations on integer data streams via numerical entanglement"):
  with members:
    - "failure mitigation in linear sesquilinear and bijective operations on integer data streams via numerical entanglement"
    - "throughput scaling of convolution for error tolerant multimedia applications"

Connected through hyperedge HE-3 (induced by source paper "failure mitigation in linear sesquilinear and bijective operations on integer data streams via numerical entanglement"):
  with members:
    - "throughput scaling of convolution for error tolerant multimedia applications"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-4 (induced by source paper "precision energy throughput scaling of generic matrix multiplication and convolution kernels via linear projections"):
  with members:
    - "throughput scaling of convolution for error tolerant multimedia applications"

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.MS(Mathematical Software)