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: "building automated vandalism detection tools for wikidata"
  Abstract: Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89% of vandalism while reducing patrollers' workload by 98%, by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit.

Connected through hyperedge HE-1 (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"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "empath understanding topic signals in large scale text"
    - "end to end memory networks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-2 (induced by source paper "wikidata from a research perspective a systematic mapping study of wikidata"):
  with members:
    - "using word embeddings for visual data exploration with ontodia and wikidata"
    - "linking imagenet wordnet synsets with wikidata"
    - "guidelines for systematic mapping studies in security engineering"
    - "wikidata vandalism detection the loganberry vandalism detector at wsdm cup 2017"
    - "overview of the wikidata vandalism detection task at wsdm cup 2017"
    - "learning to generate one sentence biographies from wikidata"
    - "predicting completeness in knowledge bases"
    - "wikidata through the eyes of dbpedia"

Connected through hyperedge HE-3 (induced by source paper "ores lowering barriers with participatory machine learning in wikipedia"):
  with members:
    - "the lives of bots"
    - "beyond opening up the black box investigating the role of algorithmic systems in wikipedian organizational culture"

Connected through hyperedge HE-4 (induced by source paper "overview of the wikidata vandalism detection task at wsdm cup 2017"):
  with members:
    - "ensemble models for detecting wikidata vandalism with stacking team honeyberry vandalism detector at wsdm cup 2017"
    - "a production oriented approach for vandalism detection in wikidata the buffaloberry vandalism detector at wsdm cup 2017"
    - "vews a wikipedia vandal early warning system"

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.IR(Information Retrieval)