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 new micro objects based evaluation measure for co clustering algorithms"
  Abstract: A new evaluation measure for co-clustering algorithms is presented.It satisfies the most exhaustive set of meta-evaluation conditions for co-clustering.Evaluation of traditional clustering is adequately handled as a particular case.All meta-evaluation conditions for traditional clustering are also satisfied. In this work, we present MOCICE-BCubed F1, a new external measure for evaluating co-clusterings, in the scenario where gold standard annotations are available for both the object clusters and the associated feature subspaces. Our proposal is an extension, using the so-called micro-objects transformation, of CICE-BCubed F1, an evaluation measure for traditional clusterings that has been proven to satisfy the most comprehensive set of meta-evaluation conditions for that task. Additionally, the proposed measure adequately handles the occurrence of overlapping in both the object and feature spaces. We prove that MOCICE-BCubed F1 satisfies the most comprehensive set of meta-evaluation conditions so far enunciated for co-clusterings. Moreover, when used for evaluating traditional clusterings, which are viewed as a particular case of co-clusterings, the proposed measure also satisfies the most comprehensive set of meta-evaluation conditions so far enunciated for the traditional task.

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)