human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "amuse empowering users for cost aware offloading with throughput delay tradeoffs"):
  Abstract: To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying Wi-Fi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for Wi-Fi access, they may not wait for Wi-Fi if they are sensitive to delay. To navigate this tradeoff, we develop Adaptive bandwidth Management through USer-Empowerment (AMUSE), a functional prototype of a practical, cost-aware Wi-Fi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and Wi-Fi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps’ content servers. Through a measurement study of 20 smartphone users’ traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users’ cellular traffic. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and Wi-Fi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility; when compared with AMUSE, other offloading algorithms yield 14 and 27 percent lower user utilities for light and heavy users, respectively. Intelligently managing users’ competing interests for cost, throughput, and delay can therefore improve their offloading decisions.

Includes members:
  Member "performance and energy efficiency of mobile data offloading with mobility prediction and prefetching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving mobile video streaming with mobility prediction and prefetching in integrated cellular wifi networks")
  Member "economics of wifi offloading trading delay for cellular capacity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards jointly optimal placement and delivery to code or not to code in wireless caching networks")
  Member "a survey of smart data pricing past proposals current plans and future trends":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "femtocell networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")

Question: Please tell me which class the source paper 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)