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 "risk aware optimization of age of information in the internet of things"):
  Abstract: Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted risk-aware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the risk-aware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal history-dependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in real-time IoT monitoring systems in a risk-aware manner.

Includes members:
  Member "optimized age of information tail for ultra reliable low latency communications in vehicular networks":
    also connected through other hyperedges:
      (none)
  Member "a reinforcement learning framework for optimizing age of information in rf powered communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who should google scholar update more often")
  Member "taming the tail of maximal information age in wireless industrial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predictive control and communication co design a gaussian process regression approach")
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "minimum age of information in the internet of things with non uniform status packet sizes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the optimality of the whittle s index policy for minimizing the age of information")
  Member "reliable transmission of short packets through queues and noisy channels under latency and peak age violation guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non linear age of information in a discrete time queue stationary distribution and average performance analysis")
  Member "joint status sampling and updating for minimizing age of information in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "age of information with gilbert elliot servers and samplers")
  Member "age of information minimization for an energy harvesting source with updating erasures with and without feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information freshness for timely detection of status changes")

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)