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: "hemingway modeling distributed optimization algorithms"
  Abstract: Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult for users as the performance and convergence rate of optimization algorithms vary with the size of the cluster. In this paper we make the case for an ML-optimizer that can select the appropriate algorithm and cluster size to use for a given problem. To do this we propose building two models: one that captures the system level characteristics of how computation, communication change as we increase cluster sizes and another that captures how convergence rates change with cluster sizes. We present preliminary results from our prototype implementation called Hemingway and discuss some of the challenges involved in developing such a system.

Connected through hyperedge HE-1 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"

Connected through hyperedge HE-2 (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"
    - "serving deep learning models in a serverless platform"
    - "serverless computing current trends and open problems"
    - "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-3 (induced by source paper "ffdl a flexible multi tenant deep learning platform"):
  with members:
    - "slaq quality driven scheduling for distributed machine learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "what does fault tolerant deep learning need from mpi"
    - "identity mappings in deep residual networks"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "trevor automatic configuration and scaling of stream processing pipelines"):
  with members:
    - "drs dynamic resource scheduling for real time analytics over fast streams"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

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)