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 "distributed learning for low latency machine type communication in a massive internet of things"):
  Abstract: The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multi-state sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delay-tolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access request and percentage of devices that learned correctly. The results show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delay-tolerant, periodic messages.

Includes members:
  Member "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cognitive information measurements a new perspective")
  Member "label less learning for traffic control in an edge network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning")
  Member "lte lte a random access for massive machine type communications in smart cities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions")
  Member "learning how to communicate in the internet of things finite resources and heterogeneity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "learning with finite memory for machine type communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cognitive hierarchy theory for distributed resource allocation in the internet of things")
  Member "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "what can wireless cellular technologies do about the upcoming smart metering traffic":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning how to communicate in the internet of things finite resources and heterogeneity")
  Member "reliable reporting for massive m2m communications with periodic resource pooling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications")

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.IT(Information Theory)