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: "dimensionality invariant similarity measure"
  Abstract: This paper presents a new similarity measure to be used for general tasks including supervised learnin g, which is represented by the K-nearest neighbor clas sifier (KNN). The proposed similarity measure is in variant to large differences in some dimensions in the feature space. The proposed metric is proved mathematicall y to be a metric. To test its viability for different applica tions, the KNN used the proposed metric for classif ying test examples chosen from a number of real datasets. Compared to some other well known metrics, the experimental results show that the proposed metric is a promisin g distance measure for the KNN classifier with stro ng potential for a wide range of applications. (Hassanat B. A. Dimensionality Invariant Similarity Measure. J Am Sci 2014;10(8):221-226). (ISSN: 1545- 1003). http://www.jofamericanscience.org . 31

Connected through hyperedge HE-1 (induced by source paper "effects of distance measure choice on k nearest neighbor classifier performance a review"):
  with members:
    - "on enhancing the performance of nearest neighbour classifiers using hassanat distance metric"
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"
    - "experiments of distance measurements in a foliage plant retrieval system"
    - "content based video retrieval systems"
    - "survey of nearest neighbor techniques"

Connected through hyperedge HE-2 (induced by source paper "on enhancing the performance of nearest neighbour classifiers using hassanat distance metric"):
  with members:
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"
    - "visual words for automatic lip reading"
    - "survey of nearest neighbor techniques"

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)