human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quote querying users as oracles in tag engines a semi supervised learning approach to personalized image tagging"):
  Abstract: Previous work has correctly identified that many of the tags that users provide on images are not visually relevant, and subsequently assume such user generated tags are noise or irrelevant. Another assumption about user generated tags is that the order of these tags provides no useful information for the prediction of tags on future images. We challenge the aforementioned assumptions, and provide a machine learning approach to the problem of personalized image tagging with the following contributions: 1.) We reformulate the personalized image tagging problem as a search/retrieval ranking problem, 2.) We leverage the order of tags, which does not always reflect visual relevance, provided by the user in the past as a cue to their tag preferences, similar to click data, 3.) We propose a technique to augment sparse user tag data (semi-supervision), and 4.) We demonstrate the efficacy of our method on a subset of Flickr images, showing improvement over our previous methods.

Includes members:
  Member "who ordered this exploiting implicit user tag order preferences for personalized image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "frozen binomials on the web word ordering and language conventions in online text")
  Member "towards understanding user preferences from user tagging behavior for personalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who ordered this exploiting implicit user tag order preferences for personalized image tagging")
  Member "deep convolutional ranking for multilabel image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learn to predict sets using feed forward neural networks")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)