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: "generating object cluster hierarchies for benchmarking"
  Abstract: The field of Machine Learning and the topic of clustering within it is still widely researched. Recently, researchers became interested in a new variant of hierarchical clustering, where hierarchical (partial order) relationships exist not only between clusters but also objects. In this variant of clustering, objects can be assigned not only to leave, but other properties are also defined. Although examples of this approach already exist in literature, the authors have encountered a problem with the analysis and comparison of obtained results. The problem is twofold. Firstly, there is a lack of evaluation methods. Secondly, there is a lack of available benchmark data, at least the authors failed to find them. The aim of this work is to fill the second gap. The main contribution of this paper is a new method of generating hierarchical structures of data. Additionally, the paper includes a theoretical analysis of the generation parameters and their influence on the results. Comprehensive experiments are presented and discussed. The dataset generator and visualiser tools developed are publicly available for use (this http URL).

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.CV(Computer Vision and Pattern Recognition)