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 "maximum mutual information regularized classification"):
  Abstract: In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reduc- ing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradi- ent descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Includes members:
  Member "document clustering based on max correntropy non negative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized maximum correntropy machine")
  Member "graph regularized non negative matrix factorization by maximizing correntropy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized maximum correntropy machine")
  Member "an evasion and counter evasion study in malicious websites detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain")
  Member "hinge loss markov random fields convex inference for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep ordinal regression for pledge specificity prediction")
  Member "adaptive learning of region based plsa model for total scene annotation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized maximum correntropy machine")
  Member "multiple graph regularized protein domain ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised learning of sparse context reconstruction coefficients for data representation and classification")

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)