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 "maximum lifetime analytics in iot networks"):
  Abstract: This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, can process (some of) the data locally, and the quality grade or performance of how data analytics tasks are carried out depends on where these are executed. The goal is to design a resource allocation algorithm that jointly maximizes the network lifetime and the performance of the data analytics tasks subject to energy constraints. This joint maximization problem is challenging with coupled resource constraints that induce non-convexity. We first show that the problem can be mapped to an equivalent convex problem, and then propose an online algorithm that provably solves the problem and does not require any a priori knowledge of the time-varying wireless link capacities and data analytics arrival process statistics. The algorithm's optimality properties are derived using an analysis which, to the best of our knowledge, proves for the first time the convergence of the dual subgradient method with time-varying sets. Our simulations seeded by real IoT device energy measurements, show that the network connectivity plays a crucial role in network lifetime maximization, that the algorithm can obtain both maximum network lifetime and maximum data analytics performance in addition to maximizing the joint objective, and that the algorithm increases the network lifetime by approximately 50% compared to an algorithm that minimizes the total energy consumption.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "stability and capacity regions or discrete time queueing networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predictive control and communication co design a gaussian process regression approach")

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)