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 "dynamic adaptive streaming using index based learning algorithms"):
  Abstract: We provide a unified framework using which we design scalable dynamic adaptive video streaming algorithms based on index based policies (dubbed DAS-IP) to maximize the Quality of Experience (QoE) provided to clients using video streaming services. Due to the distributed nature of our algorithm, it is easily implementable. #R##N#We begin by considering the simplest set-up of a one-hop wireless network in which an Access Point (AP) transmits video packets to multiple clients over a shared unreliable channel. The video file meant for each client has been fragmented into several packets, and the server maintains multiple copies (each of different quality) of the same video file. Clients maintain individual packet buffers in order to mitigate the effect of uncertainty on video iterruption. Streaming experience, or the Quality of Experience (QoE) of a client depends on several factors: i) starvation/outage probability, i.e., average time duration for which the client does not play video because the buffer is empty, ii) average video quality, iii) average number of starvation periods, iv) temporal variations in video quality etc. #R##N#We pose the problem of making dynamic streaming decisions in order to maximize the total QoE as a Constrained Markov Decision Process (CMDP). A consideration of the associated dual MDP suggests us that the problem is vastly simplified if the AP is allowed to charge a price per unit bandwidth usage from the clients. More concretely, a "client-by-client" QoE optimization leads to the networkwide QoE maximization, and thus provides us a decentralized streaming algorithm. #R##N#This enables the clients to themselves decide the optimal streaming choices in each time-slot, and yields us a much desired client-level adaptation algorithm. The optimal policy has an appealing simple threshold structure.

Includes members:
  Member "index policies for optimal mean variance trade off of inter delivery times in real time sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "closed form whittle s index enabled random access for timely status update")
  Member "a high reliability asymptotic approach for packet inter delivery time optimization in cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal age energy trade off via sleep wake scheduling")
  Member "analysis of buffer starvation with application to objective qoe optimization of streaming services":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "qoe management of multimedia streaming services in future networks a tutorial and survey")
  Member "pathwise performance of debt based policies for wireless networks with hard delay constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal information updating based on value of information")

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)