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 "robust semi supervised graph classifier learning with negative edge weights"):
  Abstract: In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In this paper, we study this classifier learning problem from a graph signal processing (GSP) perspective. Specifically, by viewing a binary classifier as a piecewise constant graph-signal in a high-dimensional feature space, we cast classifier learning as a signal restoration problem via a classical maximum a posteriori (MAP) formulation. Unlike previous graph-signal restoration works, we consider in addition edges with negative weights that signify anti-correlation between samples. One unfortunate consequence is that the graph Laplacian matrix $\mathbf{L}$ can be indefinite, and previously proposed graph-signal smoothness prior $\mathbf{x}^T \mathbf{L} \mathbf{x}$ for candidate signal $\mathbf{x}$ can lead to pathological solutions. In response, we derive an optimal perturbation matrix $\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive semi-definite, resulting in a stable signal prior. Further, instead of forcing a hard binary decision for each sample, we define the notion of generalized smoothness on graph that promotes ambiguity in the classifier signal. Finally, we propose an algorithm based on iterative reweighted least squares (IRLS) that solves the posed MAP problem efficiently. Extensive simulation results show that our proposed algorithm outperforms both SVM variants and graph-based classifiers using positive-edge graphs noticeably.

Includes members:
  Member "graph laplacian regularization for inverse imaging analysis in the continuous domain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "buildup of speaking skills in an online learning community a network analytic exploration")
  Member "accelerated graph based spectral polynomial filters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent implementations applications and extensions of the locally optimal block preconditioned conjugate gradient method lobpcg")
  Member "guided signal reconstruction with application to image magnification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guided signal reconstruction theory")
  Member "discrete signal processing on graphs sampling theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "signal recovery on graphs variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering")
  Member "active semi supervised learning using sampling theory for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "sampling large data on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "greedy sampling of graph signals")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)