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 f measure a tale of two approaches"
  Abstract: F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.

Connected through hyperedge HE-1 (induced by source paper "optimizing black box metrics with adaptive surrogates"):
  with members:
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "optimizing generalized rate metrics through game equilibrium"
    - "learning surrogate losses"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "learning to teach with dynamic loss functions"
    - "constrained classification and ranking via quantiles"
    - "learning to reweight examples for robust deep learning"
    - "online and stochastic gradient methods for non decomposable loss functions"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for set valued prediction in multi class classification"):
  with members:
    - "billion scale similarity search with gpus"
    - "lshtc a benchmark for large scale text classification"
    - "consistent algorithms for multiclass classification with a reject option"
    - "learning reductions that really work"
    - "very deep convolutional networks for large scale image recognition"
    - "conditional probability tree estimation analysis and algorithms"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-3 (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 text quantifiers for multivariate loss functions"

Connected through hyperedge HE-4 (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"
    - "on the consistency of auc pairwise optimization"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-5 (induced by source paper "learning surrogate losses"):
  with members:
    - "learning to optimize"
    - "training deep neural networks via direct loss minimization"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-6 (induced by source paper "cost sensitive feature selection by optimizing f measures"):
  with members:
    - "decomposition based transfer distance metric learning for image classification"

Connected through hyperedge HE-7 (induced by source paper "optimizing the f measure for threshold free salient object detection"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "egnet edge guidance network for salient object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "hi fi hierarchical feature integration for skeleton detection"

Connected through hyperedge HE-8 (induced by source paper "a no regret generalization of hierarchical softmax to extreme multi label classification"):
  with members:
    - "logarithmic time online multiclass prediction"
    - "fast label embeddings via randomized linear algebra"
    - "conditional probability tree estimation analysis and algorithms"
    - "deep networks with large output spaces"
    - "distributed representations of words and phrases and their compositionality"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "error correcting tournaments"

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)