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 "queue aware variable length coding for ultra reliable low latency communications with random arrival"):
  Abstract: With the phenomenal growth of the Internet of Things (IoT), Ultra Reliable Low Latency Communications (URLLC) has potentially been the enabler to guarantee the stringent requirements on latency and reliability. However, how to achieve low latency and ultra-reliability with the random arrival remains open. In this paper, a queue-aware variable-length channel coding is presented over the single URLLC user link, in which the finite blocklength of channel coding is determined based on the random arrival. More particularly, a cross-layer approach is proposed for the URLLC user to establish the optimal tradeoff between the latency and power consumption. With a probabilistic coding framework presented, the cross-layer variable-length coding can be characterized based on a Markov chain. In this way, the optimal delay-power tradeoff is given by formulating an equivalent Linear Programming (LP). By solving this LP, the delay-optimal variable-length coding can be presented based on a threshold-structure on the queue length.

Includes members:
  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-1 (induced by source paper "non linear age of information in a discrete time queue stationary distribution and average performance analysis")
  Member "joint queue aware and channel aware delay optimal scheduling of arbitrarily bursty traffic over multi state time varying channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimizing age of information with power constraints multi user opportunistic scheduling in multi state time varying channels")

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)