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 multiple access systems with queue length dependent service quality"):
  Abstract: It is commonly observed that higher workload lowers job performance. We model the workload as a queueing process and study the information-theoretic limits of reliable communication through a system with queue-length dependent service quality. The goal is to investigate a multiple-access setting, where transmitters dispatch encoded symbols over a system that is a superposition of continuous-time $GI_k/GI/1$ queues, and a noisy server, whose service quality depends on the queue-length and processes symbols in order of arrival. #R##N#We first determine the capacity of single-user queue-length dependent channels and further characterize the best and worst dispatch and service processes for $GI/M/1$ and $M/GI/1$ queues, respectively. Then we determine the multiple-access channel capacity using point processes. In particular, when the number of transmitters is large and each arrival process is sparse, the superposition of arrivals approaches a Poisson point process. In characterizing the Poisson approximation, we show that the capacity of the multiple-access system converges to the capacity of a single-user $M/GI/1$ queue-length dependent system. The speed of convergence bound in the number of users is explicitly given. Further, the best and worst server behaviors of $M/GI/1$ queues from the single-user case are preserved in the sparse multi-access case.

Includes members:
  Member "capacity of gaussian many access channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "capacity of systems with queue length dependent service quality":
    also connected through other hyperedges:
      (none)
  Member "reliable crowdsourcing for multi class labeling using coding theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "error correcting neural network")

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)