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: "a systematic comparison of supervised classifiers"
  Abstract: Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

Connected through hyperedge HE-1 (induced by source paper "paragraph based representation of texts a complex networks approach"):
  with members:
    - "an image analysis approach to text analytics based on complex networks"
    - "representation of texts as complex networks a mesoscopic approach"
    - "on the calligraphy of books"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "topic segmentation via community detection in complex networks"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "a complex network approach to stylometry"

Connected through hyperedge HE-2 (induced by source paper "human activity recognition from skeleton poses"):
  with members:
    - "rgb d based action recognition datasets a survey"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-3 (induced by source paper "word sense disambiguation a complex network approach"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"
    - "authorship attribution through function word adjacency networks"
    - "discriminating word senses with tourist walks in complex networks"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-4 (induced by source paper "on the role of words in the network structure of texts application to authorship attribution"):
  with members:
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution through function word adjacency networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-5 (induced by source paper "authorship attribution based on life like network automata"):
  with members:
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "authorship attribution through function word adjacency networks"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-6 (induced by source paper "dynamic texture analysis with diffusion in networks"):
  with members:
    - "convolutional neural network on three orthogonal planes for dynamic texture classification"
    - "spatiotemporal gabor filters a new method for dynamic texture recognition"

Connected through hyperedge HE-7 (induced by source paper "text authorship identified using the dynamics of word co occurrence networks"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "a stronger null hypothesis for crossing dependencies"

Connected through hyperedge HE-8 (induced by source paper "authorship attribution via network motifs identification"):
  with members:
    - "a complex network approach to stylometry"

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)