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: "reducing electricity demand charge for data centers with partial execution"
  Abstract: Data centers consume a large amount of energy and incur substantial electricity cost. In this paper, we study the familiar problem of reducing data center energy cost with two new perspectives. First, we find, through an empirical study of contracts from electric utilities powering Google data centers, that demand charge per kW for the maximum power used is a major component of the total cost. Second, many services such as Web search tolerate partial execution of the requests because the response quality is a concave function of processing time. Data from Microsoft Bing search engine confirms this observation. #R##N#We propose a simple idea of using partial execution to reduce the peak power demand and energy cost of data centers. We systematically study the problem of scheduling partial execution with stringent SLAs on response quality. For a single data center, we derive an optimal algorithm to solve the workload scheduling problem. In the case of multiple geo-distributed data centers, the demand of each data center is controlled by the request routing algorithm, which makes the problem much more involved. We decouple the two aspects, and develop a distributed optimization algorithm to solve the large-scale request routing problem. Trace-driven simulations show that partial execution reduces cost by $3\%--10.5\%$ for one data center, and by $15.5\%$ for geo-distributed data centers together with request routing.

Connected through hyperedge HE-1 (induced by source paper "online inventory management with application to energy procurement in data centers"):
  with members:
    - "introduction to online convex optimization"
    - "optimal posted prices for online cloud resource allocation"
    - "cost minimizing online algorithms for energy storage management with worst case guarantee"
    - "risk sensitive learning and pricing for demand response"
    - "peak aware online economic dispatching for microgrids"
    - "dynamic provisioning in next generation data centers with on site power production"
    - "optimal power cost management using stored energy in data centers"

Connected through hyperedge HE-2 (induced by source paper "learning assisted competitive algorithms for peak aware energy scheduling"):
  with members:
    - "optimal algorithms for ski rental with soft machine learned predictions"
    - "online inventory management with application to energy procurement in data centers"
    - "competitive caching with machine learned advice"
    - "online convex optimization using predictions"
    - "peak aware online economic dispatching for microgrids"

Connected through hyperedge HE-3 (induced by source paper "extending demand response to tenants in cloud data centers via non intrusive workload flexibility pricing"):
  with members:
    - "optimal power cost management using stored energy in data centers"
    - "a taxonomy and survey of energy efficient data centers and cloud computing systems"

Connected through hyperedge HE-4 (induced by source paper "group server queues"):
  with members:
    - "energy aware traffic offloading for green heterogeneous networks"
    - "multi resource energy efficient routing in cloud data centers with networks as a service"
    - "correlating power consumption and network traffic for improving data centers resiliency"
    - "profit aware server allocation for green internet services"
    - "maximizing cloud providers revenues via energy aware allocation policies"
    - "on allocation policies for power and performance"

Connected through hyperedge HE-5 (induced by source paper "fog assisted operational cost reduction for cloud data centers"):
  with members:
    - "distributed real time energy management in data center microgrids"

Connected through hyperedge HE-6 (induced by source paper "leveraging energy storage to optimize data center electricity cost in emerging power markets"):
  with members:
    - "greening multi tenant data center demand response"
    - "optimizing energy storage participation in emerging power markets"
    - "optimal power cost management using stored energy in data centers"

Connected through hyperedge HE-7 (induced by source paper "incentivizing users of data centers participate in the demand response programs via time varying monetary rewards"):
  with members:
    - "extending demand response to tenants in cloud data centers via non intrusive workload flexibility pricing"

Connected through hyperedge HE-8 (induced by source paper "peak aware online economic dispatching for microgrids"):
  with members:
    - "cost minimizing online algorithms for energy storage management with worst case guarantee"
    - "to reserve or not to reserve optimal online multi instance acquisition in iaas clouds"
    - "adaptive electricity scheduling in microgrids"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

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.NI(Networking and Internet Architecture)