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 "on the age of information in internet of things systems with correlated devices"):
  Abstract: In this paper, a real-time Internet of Things (IoT) monitoring system is considered in which multiple IoT devices must transmit timely updates on the status information of a common underlying physical process to a common destination. In particular, a real-world IoT scenario is considered in which multiple (partially) observed status information by different IoT devices are required at the destination, so that the real-time status of the physical process can be properly re-constructed. By taking into account such correlated status information at the IoT devices, the problem of IoT device scheduling is studied in order to jointly minimize the average age of information (AoI) at the destination and the average energy cost at the IoT devices. Particularly, two types of IoT devices are considered: Type-I devices whose status updates randomly arrive and type-II devices whose status updates can be generated-at-will with an associated sampling cost. This stochastic problem is formulated as an infinite horizon average cost Markov decision process (MDP). The optimal scheduling policy is shown to be threshold-based with respect to the AoI at the destination, and the threshold is non-increasing with the channel condition of each device. For a special case in which all devices are type-II, the original MDP can be reduced to an MDP with much smaller state and action spaces. The optimal policy is further shown to have a similar threshold-based structure and the threshold is non-decreasing with an energy cost function of the devices. Simulation results illustrate the structure of the optimal policy and show the effectiveness of the optimal policy compared with a myopic baseline policy.

Includes members:
  Member "age of information analysis for dynamic spectrum sharing":
    also connected through other hyperedges:
      (none)
  Member "minimizing the age of information of cognitive radio based iot systems under a collision constraint":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "age of information dependent random access for massive iot networks")
  Member "sampling for remote estimation through queues age of information and beyond":
    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 "a reinforcement learning framework for optimizing age of information in rf powered communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who should google scholar update more often")
  Member "age of information in multiple sensing of a single source":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamental limits of age of information in stationary and non stationary environments")
  Member "status from a random field how densely should one update":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revealing much while saying less predictive wireless for status update")
  Member "age of information in multiple sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")
  Member "minimizing the age of information in a csma environment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the optimality of the whittle s index policy for minimizing the age of information")

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)