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: "a performance comparison of sort and scan libraries for gpus"
  Abstract: Sorting and scanning are two fundamental primitives for constructing highly parallel algorithms. A number of libraries now provide implementations of these primitives for GPUs, but there is relatively little information about the performance of these implementations. We benchmark seven libraries for 32-bit integer scan and sort, and sorting 32-bit values by 32-bit integer keys. We show that there is a large variation in performance between the libraries, and that no one library has both optimal performance and portability.

Connected through hyperedge HE-1 (induced by source paper "accelerating reduction and scan using tensor core units"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "modeling deep learning accelerator enabled gpus"
    - "culda_cgs solving large scale lda problems on gpus"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-2 (induced by source paper "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"):
  with members:
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "multi gpu graph analytics"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-3 (induced by source paper "an efficient multiway mergesort for gpu architectures"):
  with members:
    - "merge path a visually intuitive approach to parallel merging"
    - "gpu sample sort"

Connected through hyperedge HE-4 (induced by source paper "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"):
  with members:
    - "gemmbench a framework for reproducible and collaborative benchmarking of matrix multiplication"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "cltune a generic auto tuner for opencl kernels"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

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.DC(Distributed, Parallel, and Cluster Computing)