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: "effective capacity in cognitive radio broadcast channels"
  Abstract: In this paper, we investigate effective capacity by modeling a cognitive radio broadcast channel with one secondary transmitter (ST) and two secondary receivers (SRs) under quality-of-service constraints and interference power limitations. We initially describe three different cooperative channel sensing strategies with different hard-decision combining algorithms at the ST, namely OR, Majority, and AND rules. Since the channel sensing occurs with possible errors, we consider a combined interference power constraint by which the transmission power of the secondary users (SUs) is bounded when the channel is sensed as both busy and idle. Furthermore, regarding the channel sensing decision and its correctness, there exist possibly four different transmission scenarios. We provide the instantaneous ergodic capacities of the channel between the ST and each SR in all of these scenarios. Granting that transmission outage arises when the instantaneous transmission rate is greater than the instantaneous ergodic capacity, we establish two different transmission rate policies for the SUs when the channel is sensed as idle. One of these policies features a greedy approach disregarding a possible transmission outage, and the other favors a precautious manner to prevent this outage. Subsequently, we determine the effective capacity region of this channel model, and we attain the power allocation policies that maximize this region. Finally, we present the numerical results. We first show the superiority of Majority rule when the channel sensing results are good. Then, we illustrate that a greedy transmission rate approach is more beneficial for the SUs under strict interference power constraints, whereas sending with lower rates will be more advantageous under loose interference constraints. Finally, we note that the methodology and the approach we consider in this study can be easily applied into a more general cognitive radio broadcast channel model with more than two SRs.

Connected through hyperedge HE-1 (induced by source paper "effective capacity in wireless networks a comprehensive survey"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "performance analysis of noma for ultra reliable and low latency communications"
    - "wireless access for ultra reliable low latency communication urllc principles and building blocks"
    - "secure transmission of delay sensitive data over wireless fading channels"
    - "energy efficient ofdma networks maintaining statistical qos guarantees for delay sensitive traffic"
    - "statistical qos analysis of full duplex and half duplex heterogeneous cellular networks"
    - "effective capacity of retransmission schemes a recurrence relation approach"
    - "backlog and delay reasoning in harq systems"

Connected through hyperedge HE-2 (induced by source paper "hybrid rf vlc systems under qos constraints"):
  with members:
    - "design of a cognitive vlc network with illumination and handover requirements"
    - "effective capacity in mimo channels with arbitrary inputs"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "on the capacity of free space optical intensity channels"

Connected through hyperedge HE-3 (induced by source paper "link selection in hybrid rf vlc systems under statistical queueing constraints"):
  with members:
    - "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "on the capacity of free space optical intensity channels"
    - "outage probability of the gaussian mimo free space optical channel with ppm"

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)