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: "optimizing text quantifiers for multivariate loss functions"
  Abstract: We address the problem of quantification, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or prevalence) of the class in a dataset of unlabeled items. Quantification has several applications in data and text mining, such as estimating the prevalence of positive reviews in a set of reviews of a given product or estimating the prevalence of a given support issue in a dataset of transcripts of phone calls to tech support. So far, quantification has been addressed by learning a general-purpose classifier, counting the unlabeled items that have been assigned the class, and tuning the obtained counts according to some heuristics. In this article, we depart from the tradition of using general-purpose classifiers and use instead a supervised learning model for structured prediction, capable of generating classifiers directly optimized for the (multivariate and nonlinear) function used for evaluating quantification accuracy. The experiments that we have run on 5,500 binary high-dimensional datasets (averaging more than 14,000 documents each) show that this method is more accurate, more stable, and more efficient than existing state-of-the-art quantification methods.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-2 (induced by source paper "optimizing generalized rate metrics through game equilibrium"):
  with members:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "equality of opportunity in supervised learning"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-3 (induced by source paper "semeval 2016 task 4 sentiment analysis in twitter"):
  with members:
    - "twise at semeval 2016 task 4 twitter sentiment classification"
    - "insight 1 at semeval 2016 task 4 convolutional neural networks for sentiment classification and quantification"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "evaluation measures for quantification an axiomatic approach"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "a recurrent neural network for sentiment quantification"
    - "class prior estimation for learning from positive and unlabeled data"
    - "quantification in the wild data sets and baselines"
    - "semi supervised learning of class balance under class prior change by distribution matching"

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)