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: "vews a wikipedia vandal early warning system"
  Abstract: We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential vandals so that such users can be presented early to Wikipedia administrators. We leverage multiple classical ML approaches, but develop 3 novel sets of features. Our Wikipedia Vandal Behavior (WVB) approach uses a novel set of user editing patterns as features to classify some users as vandals. Our Wikipedia Transition Probability Matrix (WTPM) approach uses a set of features derived from a transition probability matrix and then reduces it via a neural net auto-encoder to classify some users as vandals. The VEWS approach merges the previous two approaches. Without using any information (e.g. reverts) provided by other users, these algorithms each have over 85% classification accuracy. Moreover, when temporal recency is considered, accuracy goes to almost 90%. We carry out detailed experiments on a new data set we have created consisting of about 33K Wikipedia users (including both a black list and a white list of editors) and containing 770K edits. We describe specific behaviors that distinguish between vandals and non-vandals. We show that VEWS beats ClueBot NG and STiki, the best known algorithms today for vandalism detection. Moreover, VEWS detects far more vandals than ClueBot NG and on average, detects them 2.39 edits before ClueBot NG when both detect the vandal. However, we show that the combination of VEWS and ClueBot NG can give a fully automated vandal early warning system with even higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks"):
  with members:
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "stre self attentive edit quality prediction in wikipedia"):
  with members:
    - "neural based statement classification for biased language"
    - "universal language model fine tuning for text classification"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "a structured self attentive sentence embedding"
    - "building automated vandalism detection tools for wikidata"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "empath understanding topic signals in large scale text"
    - "end to end memory networks"

Connected through hyperedge HE-3 (induced by source paper "gan augmented text anomaly detection with sequences of deep statistics"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "texygen a benchmarking platform for text generation models"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-4 (induced by source paper "insider threat detection via hierarchical neural temporal point processes"):
  with members:
    - "insight into insiders and it a survey of insider threat taxonomies analysis modeling and countermeasures"
    - "learning temporal point processes via reinforcement learning"
    - "wasserstein learning of deep generative point process models"
    - "deep learning for unsupervised insider threat detection in structured cybersecurity data streams"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "ensemfdet an ensemble approach to fraud detection based on bipartite graph"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "spam review detection with graph convolutional networks"
    - "an army of me sockpuppets in online discussion communities"
    - "antisocial behavior in online discussion communities"

Connected through hyperedge HE-6 (induced by source paper "false information on web and social media a survey"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "an army of me sockpuppets in online discussion communities"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "automatic detection of fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-7 (induced by source paper "community interaction and conflict on the web"):
  with members:
    - "false information on web and social media a survey"
    - "community identity and user engagement in a multi community landscape"
    - "an army of me sockpuppets in online discussion communities"
    - "contagion dynamics of extremist propaganda in social networks"
    - "representation learning on graphs methods and applications"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "everything i disagree with is fakenews correlating political polarization and spread of misinformation"
    - "loyalty in online communities"

Connected through hyperedge HE-8 (induced by source paper "learning dynamic embeddings from temporal interactions"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "predicting interactions between individuals with structural and dynamical information"

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)