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: "empirical study of deep learning for text classification in legal document review"
  Abstract: Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.

Connected through hyperedge HE-1 (induced by source paper "an external knowledge enhanced multi label charge prediction approach with label number learning"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "self attention with relative position representations"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "sgm sequence generation model for multi label classification"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-2 (induced by source paper "empirical comparisons of cnn with other learning algorithms for text classification in legal document review"):
  with members:
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for natural language processing"
    - "very deep convolutional networks for text classification"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "image analytics for legal document review a transfer learning approach"):
  with members:
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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.IR(Information Retrieval)