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: "micro clustering finding small clusters in large diversity"
  Abstract: We address the problem of un-supervised soft-clustering called micro-clustering. The aim of the problem is to enumerate all groups composed of records strongly related to each other, while standard clustering methods separate records at sparse parts. The problem formulation of micro-clustering is non-trivial. Clique mining in a similarity graph is a typical approach, but it results in a huge number of cliques that are of many similar cliques. We propose a new concept data polishing. The cause of huge solutions can be considered that the groups are not clear in the data, that is, the boundaries of the groups are not clear, because of noise, uncertainty, lie, lack, etc. Data polishing clarifies the groups by perturbating the data. Specifically, dense subgraphs that would correspond to clusters are replaced by cliques. The clusters are clarified as maximal cliques, thus the number of maximal cliques will be drastically reduced. We also propose an efficient algorithm applicable even for large scale data. Computational experiments showed the efficiency of our algorithm, i.e., the number of solutions is small, (e.g., 1,000), the members of each group are deeply related, and the computation time is short.

Connected through hyperedge HE-1 (induced by source paper "identifier namespaces in mathematical notation"):
  with members:
    - "morphological analyzer and generator for russian and ukrainian languages"
    - "mathematical language processing project"
    - "pos tagging and its applications for mathematics"
    - "efficient estimation of word representations in vector space"
    - "machine learning in automated text categorization"

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.DS(Data Structures and Algorithms)