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 "predictive control and communication co design a gaussian process regression approach"):
  Abstract: While Remote control over wireless connections is a key enabler for scalable control systems consisting of multiple actuator-sensor pairs, i.e., control systems, it entails two technical challenges. Due to the lack of wireless resources, only a limited number of control systems can be served, making the state observations outdated. Further, even after scheduling, the state observations received through wireless channels are distorted, hampering control stability. To address these issues, in this article we propose a scheduling algorithm that guarantees the age-of-information (AoI) of the last received states. Meanwhile, for non-scheduled sensor-actuator pairs, we propose a machine learning (ML) aided predictive control algorithm, in which states are predicted using a Gaussian process regression (GPR). Since the GPR prediction credibility decreases with the AoI of the input data, both predictive control and AoI-based scheduler should be co-designed. Hence, we formulate a joint scheduling and transmission power optimization via the Lyapunov optimization framework. Numerical simulations corroborate that the proposed co-designed predictive control and AoI based scheduling achieves lower control errors, compared to a benchmark scheme using a round-robin scheduler without state prediction.

Includes members:
  Member "extreme urllc vision challenges and key enablers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "massive autonomous uav path planning a neural network based mean field game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extreme urllc vision challenges and key enablers")
  Member "taming the tail of maximal information age in wireless industrial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "risk aware optimization of age of information in the internet of things")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "joint communication and control for wireless autonomous vehicular platoon systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "wireless network design for control systems a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "urgency of information for context aware timely status updates in remote control systems")
  Member "stability and capacity regions or discrete time queueing networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal network control in partially controllable networks")

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)