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 combinatorial functions from pairwise comparisons"
  Abstract: A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples. However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set of examples. For instance, it is notoriously hard for consumers to reliably assign values to bundles of merchandise. Instead, it might be much easier for a consumer to report which of two bundles she likes better. With this motivation in mind, we consider an alternative learning model, wherein the algorithm must learn the underlying function up to pairwise comparisons, from pairwise comparisons. In this model, we present a series of novel algorithms that learn over a wide variety of combinatorial function classes. These range from graph functions to broad classes of valuation functions that are fundamentally important in microeconomic theory, the analysis of social networks, and machine learning, such as coverage, submodular, XOS, and subadditive functions, as well as functions with sparse Fourier support.

Connected through hyperedge HE-1 (induced by source paper "large scale representation learning from triplet comparisons"):
  with members:
    - "estimation of perceptual scales using ordinal embedding"
    - "effect of depth and width on local minima in deep learning"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "an efficient framework for learning sentence representations"
    - "deep metric learning with hierarchical triplet loss"
    - "the loss surface of deep and wide neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "estimation of perceptual scales using ordinal embedding"):
  with members:
    - "uncertainty estimates for ordinal embeddings"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-3 (induced by source paper "comparison based learning from weak oracles"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"
    - "comparison based learning with rank nets"
    - "adaptively learning the crowd kernel"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "approximate nearest neighbor search for low dimensional queries"
    - "hierarchical routing over dynamic wireless networks"

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)