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: "fake news detection on social media a data mining perspective"
  Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Connected through hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "discover your social identity from what you tweet a content based approach"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "exploiting multi domain visual information for fake news detection"
    - "a hierarchical location prediction neural network for twitter user geolocation"
    - "fact checking meets fauxtography verifying claims about images"
    - "false news detection on social media"
    - "aird adversarial learning framework for image repurposing detection"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-3 (induced by source paper "attributed multi relational attention network for fact checking url recommendation"):
  with members:
    - "learning from fact checkers analysis and generation of fact checking language"
    - "neural graph collaborative filtering"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "harmonious attention network for person re identification"
    - "collaborative memory network for recommendation systems"
    - "large scale learnable graph convolutional networks"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "nais neural attentive item similarity model for recommendation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "diversity density and homogeneity quantitative characteristic metrics for text collections"):
  with members:
    - "casa nlu context aware self attentive natural language understanding for task oriented chatbots"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-6 (induced by source paper "two path deep semi supervised learning for timely fake news detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "a survey on natural language processing for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "an overview of multi task learning in deep neural networks"
    - "csi a hybrid deep model for fake news detection"
    - "very deep convolutional networks for text classification"
    - "temporal ensembling for semi supervised learning"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"

Connected through hyperedge HE-7 (induced by source paper "a semi supervised approach to message stance classification"):
  with members:
    - "rumor detection and classification for twitter data"
    - "detection and resolution of rumours in social media a survey"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "using gaussian processes for rumour stance classification in social media"
    - "a tutorial on online supervised learning with applications to node classification in social networks"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-8 (induced by source paper "joint inference on truth rumor and their sources in social networks"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "automatic stance detection using end to end memory networks"
    - "necessary and sufficient budgets in information source finding with querying adaptivity gap"
    - "gemsec graph embedding with self clustering"
    - "on measuring bias in online information"
    - "rumor source detection under querying with untruthful answers"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

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)