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 "on identifying disaster related tweets matching based or learning based"):
  Abstract: Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach.

Includes members:
  Member "semi supervised discovery of informative tweets during the emerging disasters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyphrase extraction from disaster related tweets")
  Member "applications of online deep learning for crisis response using social media information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention based neural architecture for rumor detection with author context awareness")
  Member "identifying relevant messages in a twitter based citizen channel for natural disaster situations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours")
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "processing social media messages in mass emergency a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "localized flood detectionwith minimal labeled social media data using transfer learning")
  Member "is the sample good enough comparing data from twitter s streaming api with twitter s firehose":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "the new war correspondents the rise of civic media curation in urban warfare":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the contours of crowd capability")

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.IR(Information Retrieval)