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: "numpywren serverless linear algebra"
  Abstract: Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called "serverless" environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management. #R##N#We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure.

Connected through hyperedge HE-1 (induced by source paper "cloudburst stateful functions as a service"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "serverless computing one step forward two steps back"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "ray a distributed framework for emerging ai applications"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-2 (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"
    - "serving deep learning models in a serverless platform"
    - "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-3 (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"
    - "serving deep learning models in a serverless platform"
    - "occupy the cloud distributed computing for the 99"

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"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-5 (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"
    - "serving deep learning models in a serverless platform"
    - "serverless computing current trends and open problems"

Connected through hyperedge HE-6 (induced by source paper "harnessing the power of serverless runtimes for large scale optimization"):
  with members:
    - "characterizing task completion latencies in fog computing"
    - "serving deep learning models in a serverless platform"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "servermix tradeoffs and challenges of serverless data analytics"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "serverless computing one step forward two steps back"
    - "serverless data analytics with flint"
    - "comparison of faas orchestration systems"
    - "occupy the cloud distributed computing for the 99"

Connected through hyperedge HE-8 (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"
    - "benchmarking distributed stream processing engines"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "status of serverless computing and function as a service faas in industry and research"

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)