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: "on the consistency of auc pairwise optimization"
  Abstract: AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many learning approaches try to optimize AUC, while owing to the non-convexity and discontinuousness of AUC, almost all approaches work with surrogate loss functions. Thus, the consistency of AUC is crucial; however, it has been almost untouched before. In this paper, we provide a sufficient condition for the asymptotic consistency of learning approaches based on surrogate loss functions. Based on this result, we prove that exponential loss and logistic loss are consistent with AUC, but hinge loss is inconsistent. Then, we derive the $q$-norm hinge loss and general hinge loss that are consistent with AUC. We also derive the consistent bounds for exponential loss and logistic loss, and obtain the consistent bounds for many surrogate loss functions under the non-noise setting. Further, we disclose an equivalence between the exponential surrogate loss of AUC and exponential surrogate loss of accuracy, and one straightforward consequence of such finding is that AdaBoost and RankBoost are equivalent.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "mba mini batch auc optimization"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "xgboost a scalable tree boosting system"
    - "one pass auc optimization"

Connected through hyperedge HE-2 (induced by source paper "stochastic proximal auc maximization"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic variance reduction methods for saddle point problems"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "one pass auc optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "stochastic optimization for machine learning"

Connected through hyperedge HE-3 (induced by source paper "learning only from relevant keywords and unlabeled documents"):
  with members:
    - "on the convergence of adam and beyond"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "making risk minimization tolerant to label noise"
    - "adam a method for stochastic optimization"
    - "scikit learn machine learning in python"
    - "agnostic learning of monomials by halfspaces is hard"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "quadruply stochastic gradient method for large scale nonlinear semi supervised ordinal regression auc optimization"):
  with members:
    - "quadruply stochastic gradients for large scale nonlinear semi supervised auc optimization"
    - "semi supervised ordinal regression based on empirical risk minimization"
    - "scalable semi supervised svm via triply stochastic gradients"
    - "scalable kernel methods via doubly stochastic gradients"
    - "one pass auc optimization"

Connected through hyperedge HE-5 (induced by source paper "calibrated surrogate maximization of linear fractional utility in binary classification"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "beyond convexity stochastic quasi convex optimization"
    - "optimizing f measure a tale of two approaches"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-6 (induced by source paper "risk bounds for low cost bipartite ranking"):
  with members:
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"

Connected through hyperedge HE-7 (induced by source paper "learning surrogate losses"):
  with members:
    - "learning to optimize"
    - "training deep neural networks via direct loss minimization"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-8 (induced by source paper "towards deep and representation learning for talent search at linkedin"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "wide deep learning for recommender systems"
    - "a deep relevance matching model for ad hoc retrieval"
    - "xgboost a scalable tree boosting system"
    - "search by ideal candidates next generation of talent search at linkedin"
    - "personalized expertise search at linkedin"
    - "tensorflow a system for large scale machine learning"
    - "convolutional neural network architectures for matching natural language sentences"

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)