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: "computational fact checking from knowledge networks"
  Abstract: Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "hgat hierarchical graph attention network for fake news detection"):
  with members:
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "embedding logical queries on knowledge graphs"
    - "how powerful are graph neural networks"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "automatic detection of fake news"
    - "a stylometric inquiry into hyperpartisan and fake news"

Connected through hyperedge HE-3 (induced by source paper "scrutinizer a mixed initiative approach to large scale data driven claim verification"):
  with members:
    - "explainable fact checking with probabilistic answer set programming"
    - "claimrank detecting check worthy claims in arabic and english"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "automated fact checking task formulations methods and future directions"
    - "fact checking in community forums"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "the rise of social bots"
    - "believe it or not adding belief annotations to databases"

Connected through hyperedge HE-4 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "the fact extraction and verification fever shared task"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "attention is all you need"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-5 (induced by source paper "supporting early and scalable discovery of disinformation websites"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "endorsements on social media an empirical study of affiliate marketing disclosures on youtube and pinterest"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "trends in the diffusion of misinformation on social media"
    - "fake news detection on social media a data mining perspective"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"

Connected through hyperedge HE-6 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

Connected through hyperedge HE-7 (induced by source paper "taking a stance on fake news towards automatic disinformation assessment via deep bidirectional transformer language models for stance detection"):
  with members:
    - "assessing social and intersectional biases in contextualized word representations"
    - "fakta an automatic end to end fact checking system"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "measuring bias in contextualized word representations"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a retrospective analysis of the fake news challenge stance detection task"

Connected through hyperedge HE-8 (induced by source paper "multifc a real world multi domain dataset for evidence based fact checking of claims"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "end to end neural entity linking"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "twowingos a two wing optimization strategy for evidential claim verification"

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.CY(Computers and Society)