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 "a multi channel spectrum sensing fusion mechanism for cognitive radio networks design and application to ieee 802 22 wrans"):
  Abstract: The IEEE 802.22 is a new cognitive radio standard that is aimed at extending wireless outreach to rural areas. Known as wireless regional area networks, and designed based on the not-to-interfere spectrum sharing model, WRANs are channelized and centrally-controlled networks working on the under-utilized UHF/VHF TV bands to establish communication with remote users, so-called customer premises equipment (CPEs). Despite the importance of reliable and interference-free operation in these frequencies, spectrum sensing fusion mechanisms suggested in IEEE 802.22 are rudimentary and fail to satisfy the stringent mandated sensing requirements. Other deep-rooted shortcomings are performance non-uniformity over different signal-to-noise-ratio regimes, unbalanced performance, instability and lack of flexibility. Inspired by these observations, in this paper we propose a distributed spectrum sensing technique for WRANs, named multi-channel learning-based distributed sensing fusion mechanism (MC-LDS). MC-LDS is demonstrated to be self-trained, stable and to compensate for fault reports through its inherent reward-penalty approach. Moreover, MC-LDS exhibits a better uniform performance in all traffic regimes, is fair (reduces the false-alarm/misdetection gap), adjustable (works with several degrees of freedom) and bandwidth efficient (opens transmission opportunities for more CPEs). Simulation results and comparisons unanimously corroborate that MC-LDS outperforms IEEE 802.22 recommended algorithms, i.e., the AND, OR and VOTING rules.

Includes members:
  Member "maximizing system throughput by cooperative sensing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient resource allocation in eh enabled cr networks for iot")
  Member "spectrum sensing in cognitive radios based on multiple cyclic frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal multiband joint detection for spectrum sensing in cognitive radio networks")

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)