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: "improving spark application throughput via memory aware task co location a mixture of experts approach"
  Abstract: Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensitive and thus can run as batch jobs in data centers. By running multiple applications on a computing host, task co-location can significantly improve the server utilization and system throughput. However, effective task co-location is a non-trivial task, as it requires an understanding of the computing resource requirement of the co-running applications, in order to determine what tasks, and how many of them, can be co-located. #R##N#In this paper, we present a mixture-of-experts approach to model the memory behavior of Spark applications. We achieve this by learning, off-line, a range of specialized memory models on a range of typical applications; we then determine at runtime which of the memory models, or experts, best describes the memory behavior of the target application. We show that by accurately estimating the resource level that is needed, a co-location scheme can effectively determine how many applications can be co-located on the same host to improve the system throughput, by taking into consideration the memory and CPU requirements of co-running application tasks. Our technique is applied to a set of representative data analytic applications built upon the Apache Spark framework. We evaluated our approach for system throughput and average normalized turnaround time on a multi-core cluster. Our approach achieves over 83.9% of the performance delivered using an ideal memory predictor. We obtain, on average, 8.69x improvement on system throughput and a 49% reduction on turnaround time over executing application tasks in isolation, which translates to a 1.28x and 1.68x improvement over a state-of-the-art co-location scheme for system throughput and turnaround time respectively.

Connected through hyperedge HE-1 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"):
  with members:
    - "optimizing deep learning inference on embedded systems through adaptive model selection"
    - "machine learning in compiler optimisation"
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"

Connected through hyperedge HE-2 (induced by source paper "optimizing streaming parallelism on heterogeneous many core architectures"):
  with members:
    - "machine learning in compiler optimisation"
    - "hstream a directive based language extension for heterogeneous stream computing"
    - "input aware auto tuning of compute bound hpc kernels"
    - "evaluating the performance impact of multiple streams on the mic based heterogeneous platform"
    - "streaming applications on heterogeneous platforms"

Connected through hyperedge HE-3 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-4 (induced by source paper "using machine learning to optimize web interactions on heterogeneous mobile multi cores"):
  with members:
    - "pes proactive event scheduling for responsive and energy efficient mobile web computing"
    - "overcoming language dichotomies toward effective program comprehension for mobile app development"
    - "machine learning in compiler optimisation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "adaptive selection of deep learning models on embedded systems"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "daleel simplifying cloud instance selection using machine learning"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"

Connected through hyperedge HE-6 (induced by source paper "research challenges in nextgen service orchestration"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "managing service heterogeneity using osmotic computing"
    - "a comparison of reinforcement learning techniques for fuzzy cloud auto scaling"
    - "mobile cloud business process management system for the internet of things a survey"
    - "enorm a framework for edge node resource management"
    - "occupy the cloud distributed computing for the 99"
    - "on using micro clouds to deliver the fog"
    - "daleel simplifying cloud instance selection using machine learning"

Connected through hyperedge HE-7 (induced by source paper "machine learning in compiler optimisation"):
  with members:
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "deep reinforcement learning an overview"
    - "a survey of machine learning for big code and naturalness"
    - "feast an automated feature selection framework for compilation tasks"
    - "parameter free probabilistic api mining across github"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "tuning streamed applications on intel xeon phi a machine learning based approach"):
  with members:
    - "scikit learn machine learning in python"

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)