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: "status of serverless computing and function as a service faas in industry and research"
  Abstract: This whitepaper summarizes issues raised during the First International Workshop on Serverless Computing (WoSC) 2017 held June 5th 2017 and especially in the panel and associated discussion that concluded the workshop. We also include comments from the keynote and submitted papers. A glossary at the end (section 8) defines many technical terms used in this report.

Connected through hyperedge HE-1 (induced by source paper "serverless computing a survey of opportunities challenges and applications"):
  with members:
    - "no more no less a formal model for serverless computing"
    - "serverless edge computing for green oil and gas industry"
    - "serverless supercomputing high performance function as a service for science"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "numpywren serverless linear algebra"
    - "serving deep learning models in a serverless platform"

Connected through hyperedge HE-2 (induced by source paper "serverless supercomputing high performance function as a service for science"):
  with members:
    - "parsl pervasive parallel programming in python"
    - "dlhub model and data serving for science"
    - "container based cluster orchestration systems a taxonomy and future directions"
    - "a serverless tool for platform agnostic computational experiment management"
    - "serving deep learning models in a serverless platform"
    - "ray a distributed framework for emerging ai applications"
    - "serverless computing current trends and open problems"
    - "a comprehensive perspective on pilot job systems"

Connected through hyperedge HE-3 (induced by source paper "performance characterization and modeling of serverless and hpc streaming applications"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "numpywren serverless linear algebra"
    - "benchmarking distributed stream processing engines"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"

Connected through hyperedge HE-4 (induced by source paper "the server is dead long live the server rise of serverless computing overview of current state and future trends in research and industry"):
  with members:
    - "serving deep learning models in a serverless platform"

Connected through hyperedge HE-5 (induced by source paper "adaptive event dispatching in serverless computing infrastructures"):
  with members:
    - "cloudsim a novel framework for modeling and simulation of cloud computing infrastructures and services"

Connected through hyperedge HE-6 (induced by source paper "a manifesto for future generation cloud computing research directions for the next decade"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "hpc cloud for scientific and business applications taxonomy vision and research challenges"
    - "in datacenter performance analysis of a tensor processing unit"
    - "knowledge infused and consistent complex event processing over real time and persistent streams"
    - "enorm a framework for edge node resource management"
    - "serverless computing current trends and open problems"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "snafu function as a service faas runtime design and implementation"

Connected through hyperedge HE-7 (induced by source paper "serving deep learning models in a serverless platform"):
  with members:
    - "serverless computing current trends and open problems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"
    - "aggregated residual transformations for deep neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"

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)