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 "energy harvesting powered sensing in iot timeliness versus distortion"):
  Abstract: We consider an Internet-of-Things (IoT) system in which an energy harvesting powered sensor node monitors the phenomenon of interest and transmits its observations to a remote monitor over a Gaussian channel. We measure the timeliness of the signals recovered by the monitor using age of information (AoI), which could be reduced by transmitting more observations to the monitor. We evaluate the corresponding distortion with the mean-squared error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor node is random and limited, however, the timeliness and the distortion of the received signals cannot be optimized at the same time. Thus, we shall investigate the timeliness-distortion trade-off of the system by minimizing the average weighted-sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we propose a backward water-filling based offline power allocation algorithm and a genetic based offline algorithm to jointly optimize the transmission interval and transmit power. Third, we formulate the online power control as an Markov Decision Process (MDP) and solve the problem with an iterative algorithm, which closely approach the trade-off limit of the system. Also, we show that the optimal transmit power is a monotonic and bi-valued function of current AoI and distortion. Finally, we present our results via numerical simulations and extend results on the save-and-transmit policy to fading sensing systems.

Includes members:
  Member "distributed sensing with orthogonal multiple access to code or not to code":
    also connected through other hyperedges:
      (none)
  Member "timely updates in energy harvesting two hop networks offline and online policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "scheduling status updates to minimize age of information with an energy harvesting sensor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "urgency of information for context aware timely status updates in remote control systems")
  Member "optimal status update for age of information minimization with an energy harvesting source":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who should google scholar update more often")
  Member "optimal power control for transmitting correlated sources with energy harvesting constraints":
    also connected through other hyperedges:
      (none)
  Member "uplink age of information of unilaterally powered two way data exchanging systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal online transmission policy in wireless powered networks with urgency aware age of information")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "delay limited source and channel coding of quasi stationary sources over block fading channels design and scaling laws":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed sensing with orthogonal multiple access to code or not to code")

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)