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: "learning using 1 local membership queries"
  Abstract: Classic machine learning algorithms learn from labelled examples. For example, to design a machine translation system, a typical training set will consist of English sentences and their translation. There is a stronger model, in which the algorithm can also query for labels of new examples it creates. E.g, in the translation task, the algorithm can create a new English sentence, and request its translation from the user during training. This combination of examples and queries has been widely studied. Yet, despite many theoretical results, query algorithms are almost never used. One of the main causes for this is a report (Baum and Lang, 1992) on very disappointing empirical performance of a query algorithm. These poor results were mainly attributed to the fact that the algorithm queried for labels of examples that are artificial, and impossible to interpret by humans. #R##N#In this work we study a new model of local membership queries (Awasthi et al., 2012), which tries to resolve the problem of artificial queries. In this model, the algorithm is only allowed to query the labels of examples which are close to examples from the training set. E.g., in translation, the algorithm can change individual words in a sentence it has already seen, and then ask for the translation. In this model, the examples queried by the algorithm will be close to natural examples and hence, hopefully, will not appear as artificial or random. We focus on 1-local queries (i.e., queries of distance 1 from an example in the training sample). We show that 1-local membership queries are already stronger than the standard learning model. We also present an experiment on a well known NLP task of sentiment analysis. In this experiment, the users were asked to provide more information than merely indicating the label. We present results that illustrate that this extra information is beneficial in practice.

Connected through hyperedge HE-1 (induced by source paper "textual membership queries"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "text understanding from scratch"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "roget s thesaurus as a lexical resource for natural language processing"

Connected through hyperedge HE-2 (induced by source paper "distribution free learning with local queries"):
  with members:
    - "complexity theoretic limitations on learning dnf s"
    - "learning using local membership queries"

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.LG(Machine Learning)