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: "data motif based proxy benchmarks for big data and ai workloads"
  Abstract: For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation-based research. In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage this http URL

Connected through hyperedge HE-1 (induced by source paper "characterizing deep learning training workloads on alibaba pai"):
  with members:
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "graph convolutional neural networks for web scale recommender systems"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fusionstitching deep fusion and code generation for tensorflow computations on gpus"
    - "predicting the computational cost of deep learning models"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"

Connected through hyperedge HE-2 (induced by source paper "benchcouncil s view on benchmarking ai and other emerging workloads"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "mlperf inference benchmark"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "mlperf training benchmark"
    - "benchmark analysis of representative deep neural network architectures"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"

Connected through hyperedge HE-3 (induced by source paper "data motifs a lens towards fully understanding big data and ai workloads"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "hitting the memory wall implications of the obvious"

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)