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 "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"):
  Abstract: We address the problem of predicting the leading political ideology, i.e., left-center-right bias, for YouTube channels of news media. Previous work on the problem has focused exclusively on text and on analysis of the language used, topics discussed, sentiment, and the like. In contrast, here we study videos, which yields an interesting multimodal setup. Starting with gold annotations about the leading political ideology of major world news media from Media Bias/Fact Check, we searched on YouTube to find their corresponding channels, and we downloaded a recent sample of videos from each channel. We crawled more than 1,000 YouTube hours along with the corresponding subtitles and metadata, thus producing a new multimodal dataset. We further developed a multimodal deep-learning architecture for the task. Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only. We release the dataset to the research community, hoping to help advance the field of multi-modal political bias detection.

Includes members:
  Member "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting toxicity in news articles application to bulgarian")
  Member "proppy a system to unmask propaganda in online news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection")
  Member "predicting factuality of reporting and bias of news media sources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "sampling the news producers a large news and feature data set for the study of the complex media landscape":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nela gt 2019 a large multi labelled news dataset for the study of misinformation in news articles")
  Member "multi view models for political ideology detection of news articles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detecting toxicity in news articles application to bulgarian")
  Member "on measuring bias in online information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "automatic dialect detection in arabic broadcast speech":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "moroco the moldavian and romanian dialectal corpus")

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.CL(Computation and Language)