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: "monetary cost optimizations for hosting workflow as a service in iaas clouds"
  Abstract: Recently, we have witnessed workflows from science and other data-intensive applications emerging on Infrastructure-asa-Service (IaaS) clouds, and many workflow service providers offering workflow as a service (WaaS). The major concern of WaaS providers is to minimize the monetary cost of executing workflows in the IaaS cloud. While there have been previous studies on this concern, most of them assume static task execution time and static pricing scheme, and have the QoS notion of satisfying a deterministic deadline. However, cloud environment is dynamic, with performance dynamics caused by the interference from concurrent executions and price dynamics like spot prices offered by Amazon EC2. Therefore, we argue that WaaS providers should have the notion of offering probabilistic performance guarantees for individual workflows on IaaS clouds. We develop a probabilistic scheduling framework called Dyna to minimize the monetary cost while offering probabilistic deadline guarantees. The framework includes an A*-based instance configuration method for performance dynamics, and a hybrid instance configuration refinement for utilizing spot instances. Experimental results with three real-world scientific workflow applications on Amazon EC2 demonstrate (1) the accuracy of our framework on satisfying the probabilistic deadline guarantees required by the users; (2) the effectiveness of our framework on reducing monetary cost in comparison with the existing approaches.

Connected through hyperedge HE-1 (induced by source paper "multiple workflows scheduling in multi tenant distributed systems a taxonomy and future directions"):
  with members:
    - "predictive performance modeling for distributed batch processing using black box monitoring and machine learning"
    - "container solutions for hpc systems a case study of using shifter on blue waters"
    - "task runtime prediction in scientific workflows using an online incremental learning approach"
    - "scalability of vm provisioning systems"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"
    - "internet of things iot a vision architectural elements and future directions"
    - "a taxonomy and survey of energy efficient data centers and cloud computing systems"
    - "a taxonomy of workflow management systems for grid computing"

Connected through hyperedge HE-2 (induced by source paper "the many faces of data centric workflow optimization a survey"):
  with members:
    - "cost optimization of data flows based on task re ordering"
    - "opening the black boxes in data flow optimization"
    - "stubby a transformation based optimizer for mapreduce workflows"

Connected through hyperedge HE-3 (induced by source paper "exploiting data reduction principles in cloud based data management for cryo image data"):
  with members:
    - "recent developments in cloud based systems state of art"
    - "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment"

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)