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: "construction of fuzzyfind dictionary using golay coding transformation for searching applications"
  Abstract: searching through a large volume of data is very critical for companies, scientists, and searching engines applications due to time complexity and memory complexity. In this paper, a new technique of generating FuzzyFind Dictionary for text mining was introduced. We simply mapped the 23 bits of the English alphabet into a FuzzyFind Dictionary or more than 23 bits by using more FuzzyFind Dictionary, and reflecting the presence or absence of particular letters. This representation preserves closeness of word distortions in terms of closeness of the created binary vectors within Hamming distance of 2 deviations. This paper talks about the Golay Coding Transformation Hash Table and how it can be used on a FuzzyFind Dictionary as a new technology for using in searching through big data. This method is introduced by linear time complexity for generating the dictionary and constant time complexity to access the data and update by new data sets, also updating for new data sets is linear time depends on new data points. This technique is based on searching only for letters of English that each segment has 23 bits, and also we have more than 23-bit and also it could work with more segments as reference table.

Connected through hyperedge HE-1 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "analysis of railway accidents narratives using deep learning"
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (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"
    - "novel metaknowledge based processing technique for multimediata big data clustering challenges"
    - "23 bit metaknowledge template towards big data knowledge discovery and management"

Connected through hyperedge HE-3 (induced by source paper "hdltex hierarchical deep learning for text classification"):
  with members:
    - "gakco a fast gapped k mer string kernel using counting"
    - "deep motif dashboard visualizing and understanding genomic sequences using deep neural networks"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "character level convolutional networks for text classification"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "a novel recommendation system to match college events and groups to students"):
  with members:
    - "hdltex hierarchical deep learning for text classification"
    - "efficient estimation of word representations in vector space"

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)