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 "svm and dimensionality reduction in cognitive radio with experimental validation"):
  Abstract: There is a trend of applying machine learning algorithms to cognitive radio. One fundamental open problem is to determine how and where these algorithms are useful in a cognitive radio network. In radar and sensing signal processing, the control of degrees of freedom (DOF)---or dimensionality---is the first step, called pre-processing. In this paper, the combination of dimensionality reduction with SVM is proposed apart from only applying SVM for classification in cognitive radio. Measured Wi-Fi signals with high signal to noise ratio (SNR) are employed to the experiments. The DOF of Wi-Fi signals is extracted by dimensionality reduction techniques. Experimental results show that with dimensionality reduction, the performance of classification is much better with fewer features than that of without dimensionality reduction. The error rates of classification with only one feature of the proposed algorithm can match the error rates of 13 features of the original data. The proposed method will be further tested in our cognitive radio network testbed.

Includes members:
  Member "spectrum sensing for cognitive radio using kernel based learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized fmd detection for spectrum sensing under low signal to noise ratio")
  Member "principal manifolds and nonlinear dimension reduction via local tangent space alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differential similarity in higher dimensional spaces theory and applications")

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)