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: "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
  Abstract: In recent years fused-multiply-add (FMA) units with lower-precision multiplications and higher-precision accumulation have proven useful in machine learning/artificial intelligence applications, most notably in training deep neural networks due to their extreme computational intensity. Compared to classical IEEE-754 32 bit (FP32) and 64 bit (FP64) arithmetic, these reduced precision arithmetic can naturally be sped up disproportional to their shortened width. The common strategy of all major hardware vendors is to aggressively further enhance their performance disproportionately. One particular FMA operation that multiplies two BF16 numbers while accumulating in FP32 has been found useful in deep learning, where BF16 is the 16-bit floating point datatype with IEEE FP32 numerical range but 8 significant bits of precision. In this paper, we examine the use this FMA unit to implement higher-precision matrix routines in terms of potential performance gain and implications on accuracy. We demonstrate how a decomposition into multiple smaller datatypes can be used to assemble a high-precision result, leveraging the higher precision accumulation of the FMA unit. We first demonstrate that computations of vector inner products and by natural extension, matrix-matrix products can be achieved by decomposing FP32 numbers in several BF16 numbers followed by appropriate computations that can accommodate the dynamic range and preserve accuracy compared to standard FP32 computations, while projecting up to 5.2x speed-up. Furthermore, we examine solution of linear equations formulated in the residual form that allows for iterative refinement. We demonstrate that the solution obtained to be comparable to those offered by FP64 under a large range of linear system condition numbers.

Connected through hyperedge HE-1 (induced by source paper "large scale discrete fourier transform on tpus"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "a flexible framework for parallel multi dimensional dfts"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "tensor processing units for financial monte carlo"
    - "in datacenter performance analysis of a tensor processing unit"
    - "a berkeley view of systems challenges for ai"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

Connected through hyperedge HE-2 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "continual lifelong learning with neural networks a review"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "online deep learning learning deep neural networks on the fly"

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)