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: "serving deep learning models in a serverless platform"
  Abstract: Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.

Connected through hyperedge HE-1 (induced by source paper "faasm lightweight isolation for efficient stateful serverless computing"):
  with members:
    - "not so fast analyzing the performance of webassembly vs native code"
    - "serverless computing one step forward two steps back"
    - "numpywren serverless linear algebra"
    - "ray a distributed framework for emerging ai applications"
    - "occupy the cloud distributed computing for the 99"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "infinicache exploiting ephemeral serverless functions to build a cost effective memory cache"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "in search of a fast and efficient serverless dag engine"
    - "size aware sharding for improving tail latencies in in memory key value stores"
    - "numpywren serverless linear algebra"
    - "occupy the cloud distributed computing for the 99"

Connected through hyperedge HE-3 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "a first look at deep learning apps on smartphones"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "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"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"

Connected through hyperedge HE-5 (induced by source paper "perseus characterizing performance and cost of multi tenant serving for cnn models"):
  with members:
    - "barista efficient and scalable serverless serving system for deep learning prediction services"
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "dynamic space time scheduling for gpu inference"
    - "mlperf inference benchmark"
    - "infaas a model less inference serving system"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "auto tuning neural network quantization framework for collaborative inference between the cloud and edge"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"

Connected through hyperedge HE-6 (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"
    - "serverless computing current trends and open problems"

Connected through hyperedge HE-7 (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"
    - "ray a distributed framework for emerging ai applications"
    - "serverless computing current trends and open problems"
    - "status of serverless computing and function as a service faas in industry and research"
    - "a comprehensive perspective on pilot job systems"

Connected through hyperedge HE-8 (induced by source paper "harnessing the power of serverless runtimes for large scale optimization"):
  with members:
    - "characterizing task completion latencies in fog computing"
    - "numpywren serverless linear algebra"
    - "serverless computing current trends and open problems"
    - "hemingway modeling distributed optimization algorithms"
    - "revisiting distributed synchronous sgd"
    - "a sequential approximation framework for coded distributed optimization"
    - "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"

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)