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 "scheduling observers over a shared channel with hard delivery deadlines"):
  Abstract: We abstract the core logical functions from applications that require ultra-low-latency wireless communications to provide a novel definition for reliability. Real-time applications -- such as intelligent transportation, remote surgery, and industrial automation -- involve a significant element of control and decision making. Such systems involve three logical components: observers (e.g. sensors) measuring the state of an environment or dynamical system, a centralized executive (e.g. controller) deciding on the state, and agents (e.g. actuators) that implement the executive's decisions. The executive harvests the observers' measurements and decides on the short-term trajectory of the system by instructing its agents to take appropriate actions. All observation packets (typically uplink) and action packets (typically downlink) must be delivered by hard deadlines to ensure the proper functioning of the controlled system. In-full on-time delivery cannot be guaranteed in wireless systems due to inherent uncertainties in the channel such as fading and unpredictable interference; accordingly, the executive will have to drop some packets. We develop a novel framework to formulate the observer selection problem (OSP) through which the executive schedules a sequence of observations that maximize its knowledge about the current state of the system. To solve this problem efficiently yet optimally, we devise a branch-and-bound algorithm that systematically prunes the search space. Our work is different from existing work on real-time communications in that communication reliability is not conveyed by packet loss or error rate, but rather by the extent of the executive's knowledge about the state of the system it controls.

Includes members:
  Member "self driving cars a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "resource allocation and harq optimization for urllc traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "5g wireless network slicing for embb urllc and mmtc a communication theoretic view":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "joint uplink and downlink resource configuration for ultra reliable and low latency communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access based reliable uplink communication and power transfer using dynamic power splitting")
  Member "cross layer optimization for ultra reliable and low latency radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint")
  Member "joint scheduling of urllc and embb traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")

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)