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: "novel metaknowledge based processing technique for multimediata big data clustering challenges"
  Abstract: Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between data points based on met features extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to be processed in 23-bit Golay Code for cluster recognition.

Connected through hyperedge HE-1 (induced by source paper "fsl bm fuzzy supervised learning with binary meta feature for classification"):
  with members:
    - "a novel recommendation system to match college events and groups to students"
    - "hdltex hierarchical deep learning for text classification"
    - "an efficient evolutionary based method for image segmentation"
    - "weighted unsupervised learning for 3d object detection"
    - "construction of fuzzyfind dictionary using golay coding transformation for searching applications"
    - "23 bit metaknowledge template towards big data knowledge discovery and management"

Connected through hyperedge HE-2 (induced by source paper "weighted unsupervised learning for 3d object detection"):
  with members:
    - "23 bit metaknowledge template towards big data knowledge discovery and management"
    - "comparison three methods of clustering k means spectral clustering and hierarchical clustering"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "construction of fuzzyfind dictionary using golay coding transformation for searching applications"):
  with members:
    - "23 bit metaknowledge template towards big data knowledge discovery and management"
    - "comparison three methods of clustering k means spectral clustering and hierarchical clustering"

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.DB(Databases)