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: "daleel simplifying cloud instance selection using machine learning"
  Abstract: Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning software configuration spaces a systematic literature review"):
  with members:
    - "automated search for configurations of deep neural network architectures"
    - "machine learning meets quantitative planning enabling self adaptation in autonomous robots"
    - "finding faster configurations using flash"
    - "towards adversarial configurations for software product lines"
    - "an efficient method for uncertainty propagation in robust software performance estimation"
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "on the relation of external and internal feature interactions a case study"

Connected through hyperedge HE-3 (induced by source paper "transferable knowledge for low cost decision making in cloud environments"):
  with members:
    - "cloud brokerage a systematic survey"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"
    - "cloud benchmarking for performance"

Connected through hyperedge HE-4 (induced by source paper "on using retrained and incremental machine learning for modeling performance of adaptable software an empirical comparison"):
  with members:
    - "a survey and taxonomy of self aware and self adaptive cloud autoscaling systems"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "transfer learning for improving model predictions in highly configurable software"

Connected through hyperedge HE-5 (induced by source paper "slo ml a language for service level objective modelling in multi cloud applications"):
  with members:
    - "transferable knowledge for low cost decision making in cloud environments"
    - "price and performance of cloud hosted virtual network functions analysis and future challenges"
    - "cloud brokerage a systematic survey"

Connected through hyperedge HE-6 (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"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"

Connected through hyperedge HE-7 (induced by source paper "cloud brokerage a systematic survey"):
  with members:
    - "price and performance of cloud hosted virtual network functions analysis and future challenges"
    - "same same but different a descriptive differentiation of intra cloud iaas services"
    - "socloud a service oriented component based paas for managing portability provisioning elasticity and high availability across multiple clouds"
    - "container based cloud virtual machine benchmarking"
    - "cloud services brokerage a survey and research roadmap"
    - "academic cloud computing research five pitfalls and five opportunities"
    - "experiences of using a hybrid cloud to construct an environmental virtual observatory"
    - "vm mad a cloud cluster software for service oriented academic environments"

Connected through hyperedge HE-8 (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"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"

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)