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 "glioma grade predictions using scattering wavelet transform based radiomics"):
  Abstract: Glioma grading before the surgery is very critical for the prognosis prediction and treatment plan making. In this paper, we present a novel scattering wavelet-based radiomics method to predict noninvasively and accurately the glioma grades. The multimodal magnetic resonance images of 285 patients were used, with the intratumoral and peritumoral regions well labeled. The wavelet scattering-based features and traditional radiomics features were firstly extracted from both intratumoral and peritumoral regions respectively. The support vector machine (SVM), logistic regression (LR) and random forest (RF) were then trained with 5-fold cross validation to predict the glioma grades. The prediction obtained with different features was finally evaluated in terms of quantitative metrics. The area under the receiver operating characteristic curve (AUC) of glioma grade prediction based on scattering wavelet features was up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which increases by about 17% compared to traditional radiomics. Such results shown that the local invariant features extracted from the scattering wavelet transform allows improving the prediction accuracy for glioma grading. In addition, the features extracted from peritumoral regions further increases the accuracy of glioma grading.

Includes members:
  Member "generic deep networks with wavelet scattering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural 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.LG(Machine Learning)