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 "regularized maximum correntropy machine"):
  Abstract: In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two chal- lenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

Includes members:
  Member "a characterization of cybersecurity posture from network telescope data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on security metrics")
  Member "semantic sparse recoding of visual content for image applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal multi scale deep learning for large scale image annotation")
  Member "document clustering based on max correntropy non negative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image tag completion by local learning")
  Member "graph regularized non negative matrix factorization by maximizing correntropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximum mutual information regularized classification")
  Member "maximum mutual information regularized classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "general information bottleneck objectives and their applications to machine learning")
  Member "an evasion and counter evasion study in malicious websites detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain")
  Member "adaptive learning of region based plsa model for total scene annotation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maximum mutual information regularized classification")
  Member "latent semantic learning with structured sparse representation for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "face focused cross stream network for deception detection in videos")

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)