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: "on enhancing the performance of nearest neighbour classifiers using hassanat distance metric"
  Abstract: We showed in this work how the Hassanat distance metric enhances the performance of the nearest neighb our classifiers. The results demonstrate the superiority of this distanc e metric over the traditional and most-used distanc es, such as Manhattan distance and Euclidian distance. Moreover, we prove d that the Hassanat distance metric is invariant to data scale, noise and outliers. Throughout this work, it is clearly notab le that both ENN and IINC performed very well with the distance investigated, as their accuracy increased significantly by 3.3% a nd 3.1% respectively, with no significant advantage of the ENN over the IINC in terms of accuracy. Correspondingly, it can be no ted from our results that there is no optimal algor ithm that can solve all reallife problems perfectly; this is supported by the n o-free-lunch theorem.

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