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: "primary user traffic estimation for dynamic spectrum access"
  Abstract: Accurate estimation of licensed channel Primary User's (PU) temporal statistics is important for Dynamic Spectrum Access (DSA) systems. With accurate estimation of the mean duty cycle, u, and the mean off- and on-times of PUs, DSA systems can more efficiently assign PU resources to its subscribers, thus, increasing channel utilization. This paper presents a mathematical analysis of the accuracy of estimating u, as well as the PU mean off- and on-times, where the estimation accuracy is expressed as the mean squared estimation error. The analysis applies for the traffic model assuming exponentially distributed PU off- and on-times, which is a common model in traffic literature. The estimation accuracy is quantified as a function of the number of samples and observation window length, hence, this work provides guidelines on traffic parameters estimation for both energy-constrained and delay-constrained applications. For estimating u, we consider uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood estimation. The estimation accuracy of the mean PU off- and on-times is studied when maximum likelihood estimation is employed. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions. We show that the estimation error for all traffic parameters is lower bounded for a fixed observation window length due to the correlation between the traffic samples. Moreover, we prove that for estimating u, maximum likelihood estimation can yield the same estimation error as weighted sample averaging using only half the observation window length.

Connected through hyperedge HE-1 (induced by source paper "spectral and energy efficiency in cognitive radio systems with unslotted primary users and sensing uncertainty"):
  with members:
    - "sensing throughput tradeoff for interweave cognitive radio system a deployment centric viewpoint"
    - "sensing throughput tradeoff in cognitive radio with random arrivals and departures of multiple primary users"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"

Connected through hyperedge HE-2 (induced by source paper "robust cooperative spectrum sensing scheduling optimization in multi channel dynamic spectrum access networks"):
  with members:
    - "distributed spectrum sensing with sequential ordered transmissions to a cognitive fusion center"

Connected through hyperedge HE-3 (induced by source paper "when channel bonding is beneficial for opportunistic spectrum access networks"):
  with members:
    - "analysis framework for opportunistic spectrum ofdma and its application to the ieee 802 22 standard"
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-4 (induced by source paper "priority queueing models for cognitive radio networks with traffic differentiation"):
  with members:
    - "delay analysis of multichannel opportunistic spectrum access mac protocols"
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "cognitive medium access exploration exploitation and competition"

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.NI(Networking and Internet Architecture)