human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "joint status sampling and updating for minimizing age of information in the internet of things"
  Abstract: The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained MDP using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.

Connected through hyperedge HE-1 (induced by source paper "age of information with gilbert elliot servers and samplers"):
  with members:
    - "partial updates losing information for freshness"
    - "optimal selective encoding for timely updates"
    - "who should google scholar update more often"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "age of information for discrete time queues"
    - "on timely channel coding with hybrid arq"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"

Connected through hyperedge HE-2 (induced by source paper "on the age of information in internet of things systems with correlated devices"):
  with members:
    - "age of information analysis for dynamic spectrum sharing"
    - "minimizing the age of information of cognitive radio based iot systems under a collision constraint"
    - "sampling for remote estimation through queues age of information and beyond"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information in multiple sensing of a single source"
    - "status from a random field how densely should one update"
    - "age of information in multiple sensing"
    - "minimizing the age of information in a csma environment"

Connected through hyperedge HE-3 (induced by source paper "analysis on computation intensive status update in mobile edge computing"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "age of information for computation intensive messages in mobile edge computing"
    - "timely cloud computing preemption and waiting"
    - "age of information in a network of preemptive servers"
    - "the age of information in networks moments distributions and sampling"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "a survey on mobile edge computing the communication perspective"
    - "the age of information in multihop networks"

Connected through hyperedge HE-4 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"

Connected through hyperedge HE-5 (induced by source paper "risk aware optimization of age of information in the internet of things"):
  with members:
    - "optimized age of information tail for ultra reliable low latency communications in vehicular networks"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "taming the tail of maximal information age in wireless industrial networks"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "minimum age of information in the internet of things with non uniform status packet sizes"
    - "reliable transmission of short packets through queues and noisy channels under latency and peak age violation guarantees"
    - "age of information minimization for an energy harvesting source with updating erasures with and without feedback"
    - "when to preempt age of information minimization under link capacity constraint"

Connected through hyperedge HE-6 (induced by source paper "minimizing the age of information of cognitive radio based iot systems under a collision constraint"):
  with members:
    - "minimizing age of information for real time monitoring in resource constrained industrial iot networks"
    - "to skip or to switch minimizing age of information under link capacity constraint"
    - "spectrum sharing for internet of things a survey"
    - "wireless network design for control systems a survey"
    - "average age of information with hybrid arq under a resource constraint"
    - "update or wait how to keep your data fresh"
    - "the emerging internet of things marketplace from an industrial perspective a survey"

Connected through hyperedge HE-7 (induced by source paper "on the age of information for multicast transmission with hard deadlines in iot systems"):
  with members:
    - "minimum age of information in the internet of things with non uniform status packet sizes"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "multicast with prioritized delivery how fresh is your data"
    - "status updates through multicast networks"
    - "on the age of information in status update systems with packet management"

Connected through hyperedge HE-8 (induced by source paper "timely distributed computation with stragglers"):
  with members:
    - "straggler mitigation at scale"
    - "trading off computation with transmission in status update systems"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "sampling for remote estimation through queues age of information and beyond"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "age of information for computation intensive messages in mobile edge computing"
    - "age of information for discrete time queues"
    - "on timely channel coding with hybrid arq"

Question: Please tell me which class the center node 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)