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: "accelerating reduction and scan using tensor core units"
  Abstract: Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as Tensor Core Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4 × 4 or 16 × 16) to accelerate HPC and deep learning workloads. Although TCUs are prevalent and promise increase in performance and/or energy efficiency, they suffer from over specialization as only matrix multiplication on small matrices is supported. In this paper we express both reduction and scan in terms of matrix multiplication operations and map them onto TCUs. To our knowledge, this paper is the first to try to broaden the class of algorithms expressible as TCU operations and is the first to show benefits of this mapping in terms of: program simplicity, efficiency, and performance. We implemented the reduction and scan algorithms using NVIDIA's V100 TCUs and achieved 89% -- 98% of peak memory copy bandwidth. Our results are orders of magnitude faster (up to 100 × for reduction and 3 × for scan) than state-of-the-art methods for small segment sizes (common in HPC and deep learning applications). Our implementation achieves this speedup while decreasing the power consumption by up to 22% for reduction and 16% for scan.

Connected through hyperedge HE-1 (induced by source paper "gpu tensor cores for fast arithmetic reductions"):
  with members:
    - "dissecting the nvidia turing t4 gpu via microbenchmarking"
    - "gpu parallel simulation algorithm of brownian particles with excluded volume using delaunay triangulations"
    - "nvidia tensor core programmability performance precision"
    - "exploration of low numeric precision deep learning inference using intel fpgas"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "a computational model for tensor core units"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "modeling deep learning accelerator enabled gpus"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "in datacenter performance analysis of a tensor processing unit"
    - "benchmarking state of the art deep learning software tools"
    - "identity mappings in deep residual networks"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "high accuracy low precision qr factorization and least square solver on gpu with tensorcore"):
  with members:
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"

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.PF(Performance)