human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nass ai towards digitization of parliamentary bills using document level embedding and bidirectional long short term memory"):
  Abstract: There has been several reports in the Nigerian and International media about the Senators and House of Representative Members of the Nigerian National Assembly (NASS) being the highest paid in the world. Despite this high-level of parliamentary compensation and a lack of oversight, most of the legislative duties like bills introduced and vote proceedings are shrouded in mystery without an open and annotated corpus. In this paper, we present results from ongoing research on the categorization of bills introduced in the Nigerian parliament since the fourth republic (1999 - 2018). For this task, we employed a multi-step approach which involves extracting text from scanned and embedded pdfs with low to medium quality using Optical Character Recognition (OCR) tools and labeling them into eight categories. We investigate the performance of document level embedding for feature representation of the extracted texts before using a Bidirectional Long Short-Term Memory (Bi-LSTM) for our classifier. The performance was further compared with other feature representation and machine learning techniques. We believe that these results are well-positioned to have a substantial impact on the quest to meet the basic open data charter principles.

Includes members:
  Member "analysis of speeches in indian parliamentary debates":
    also connected through other hyperedges:
      (none)
  Member "predicting and understanding law making with word vectors and an ensemble model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "openedgar open source software for sec edgar analysis")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)