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 "self organized low power iot networks a distributed learning approach"):
  Abstract: Enabling large-scale energy-efficient Internet-of-things (IoT) connectivity is an essential step towards realization of networked society. While legacy wide-area wireless systems are highly dependent on network-side coordination, the level of consumed energy in signaling, as well as the expected increase in the number of IoT devices, makes such centralized approaches infeasible in future. Here, we address this problem by self-coordination for IoT networks through learning from past communications. To this end, we first study low-complexity distributed learning approaches applicable in IoT communications. Then, we present a learning solution to adapt communication parameters of devices to the environment for maximizing energy efficiency and reliability in data transmissions. Furthermore, leveraging tools from stochastic geometry, we evaluate the performance of proposed distributed learning solution against the centralized coordination. Finally, we analyze the interplay amongst energy efficiency, reliability of communications against noise and interference over data channel, and reliability against adversarial interference over data and feedback channels. The simulation results indicate that compared to the state of the art approaches, both energy efficiency and reliability in IoT communications could be significantly improved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.

Includes members:
  Member "iot security techniques based on machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning in flipit")
  Member "optimized resource provisioning and operation control for low power wide area iot networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance evaluation and optimization of lpwa iot networks a stochastic geometry approach")
  Member "grant free radio access iot networks scalability analysis in coexistence scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "space diversity based grant free random access for critical and non critical iot services")
  Member "low power wide area network analysis can lora scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan")
  Member "energy efficient scheduling and grouping for machine type communications over cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimized resource provisioning and operation control for low power wide area iot networks")
  Member "the best of both worlds stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bandits with adversarial scaling")

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)