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 "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.

Includes members:
  Member "a cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "artificial intelligence based cognitive routing for cognitive radio networks")
  Member "exploiting channel correlation and pu traffic memory for opportunistic spectrum scheduling":
    also connected through other hyperedges:
      (none)
  Member "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel mac protocol for full duplex cognitive radio networks with optimized access control and load balancing")
  Member "throughput and collision analysis of multichannel multistage spectrum sensing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test")
  Member "sensitive white space detection with spectral covariance sensing":
    also connected through other hyperedges:
      (none)

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.NI(Networking and Internet Architecture)