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 "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  Abstract: Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a three-fold reduction in latency compared to a random scheduling policy since it prioritises the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm can provide system fairness by allowing the most important MTDs to be scheduled more often while also allowing the less important MTDs to be selected enough times to ensure the accuracy of estimation of their importance.

Includes members:
  Member "fast uplink grant for machine type communications challenges and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massive access in beyond 5g iot networks with noma np hardness competitiveness and learning")
  Member "deep learning for signal authentication and security in massive internet of things systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks")
  Member "random access schemes in wireless systems with correlated user activity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual bandit learning for machine type communications in the null space of multi antenna systems")
  Member "deep learning for reliable mobile edge analytics in intelligent transportation systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal time allocation in vanets advertising a price based approach using stackelberg game")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "deep learning based dynamic watermarking for secure signal authentication in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things")
  Member "distributed user association in energy harvesting small cell networks a probabilistic bandit model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient optimization of wireless powered 5g full duplex cellular networks a mean field game approach")
  Member "cognitive hierarchy theory for distributed resource allocation in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "resource management techniques for cloud based iot environment")

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)