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: "automatic rumor detection on microblogs a survey"
  Abstract: The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.

Connected through hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "generalizing from a few examples a survey on few shot learning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "defending against neural fake news"
    - "studying fake news via network analysis detection and mitigation"
    - "hierarchical propagation networks for fake news detection investigation and exploitation"

Connected through hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-4 (induced by source paper "news labeling as early as possible real or fake"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "fake news a survey of research detection methods and opportunities"
    - "learning class specific word representations for early detection of hoaxes in social media"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "detection and resolution of rumours in social media a survey"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-5 (induced by source paper "rumor detection on social media datasets methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"
    - "spotting rumors via novelty detection"

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.SI(Social and Information Networks)