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 "attention based neural architecture for rumor detection with author context awareness"):
  Abstract: The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter. The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. To derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results.

Includes members:
  Member "toward extractive summarization of online forum discussions via hierarchical attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization")
  Member "call attention to rumors deep attention based recurrent neural networks for early rumor detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combating fake news a survey on identification and mitigation techniques")
  Member "quantifying mental health from social media with neural user embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fusion strategies for learning user embeddings with neural networks")
  Member "a genetic programming approach to designing convolutional neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "csi a hybrid deep model for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "modelling context with user embeddings for sarcasm detection in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a transformer based approach to irony and sarcasm detection")
  Member "applications of online deep learning for crisis response using social media information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot tweet detection in emerging disaster events")
  Member "stance detection with bidirectional conditional encoding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains")

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.SI(Social and Information Networks)