human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "general information bottleneck objectives and their applications to machine learning"):
  with members:
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "tightening mutual information based bounds on generalization error"
    - "variational predictive information bottleneck"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "opening the black box of deep neural networks via information"
    - "optimal causal inference estimating stored information and approximating causal architecture"

Connected through hyperedge HE-2 (induced by source paper "supervised learning of sparse context reconstruction coefficients for data representation and classification"):
  with members:
    - "regularized maximum correntropy machine"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-3 (induced by source paper "regularized maximum correntropy machine"):
  with members:
    - "a characterization of cybersecurity posture from network telescope data"
    - "semantic sparse recoding of visual content for image applications"
    - "document clustering based on max correntropy non negative matrix factorization"
    - "graph regularized non negative matrix factorization by maximizing correntropy"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "latent semantic learning with structured sparse representation for human action recognition"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-4 (induced by source paper "supervised cross modal factor analysis for multiple modal data classification"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "an evasion and counter evasion study in malicious websites detection"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-5 (induced by source paper "preprint webvrgis based traffic analysis and visualization system"):
  with members:
    - "regularized maximum correntropy machine"

Question: Please tell me which class the center node 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)