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: "online and stochastic gradient methods for non decomposable loss functions"
  Abstract: Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel challenges in terms of algorithm design and analysis. In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable incremental learning as well as design scalable solvers for batch problems. To this end, we propose an online learning framework for such loss functions. Our model enjoys several nice properties, chief amongst them being the existence of efficient online learning algorithms with sublinear regret and online to batch conversion bounds. Our model is a provable extension of existing online learning models for point loss functions. We instantiate two popular losses, prec@k and pAUC, in our model and prove sublinear regret bounds for both of them. Our proofs require a novel structural lemma over ranked lists which may be of independent interest. We then develop scalable stochastic gradient descent solvers for non-decomposable loss functions. We show that for a large family of loss functions satisfying a certain uniform convergence property (that includes prec@k, pAUC, and F-measure), our methods provably converge to the empirical risk minimizer. Such uniform convergence results were not known for these losses and we establish these using novel proof techniques. We then use extensive experimentation on real life and benchmark datasets to establish that our method can be orders of magnitude faster than a recently proposed cutting plane method.

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"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-2 (induced by source paper "addressing the loss metric mismatch with adaptive loss alignment"):
  with members:
    - "autoloss learning discrete schedules for alternate optimization"
    - "deep bilevel learning"
    - "learning to teach with dynamic loss functions"
    - "cosface large margin cosine loss for deep face recognition"
    - "snas stochastic neural architecture search"
    - "learning to reweight examples for robust deep learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-3 (induced by source paper "cosegnet deep co segmentation of 3d shapes with group consistency loss"):
  with members:
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "deep object co segmentation"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d shape segmentation via shape fully convolutional networks"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "tags2parts discovering semantic regions from shape tags"

Connected through hyperedge HE-4 (induced by source paper "optimizing nondecomposable data dependent regularizers via lagrangian reparameterization offers significant performance and efficiency gains"):
  with members:
    - "differentiable sorting using optimal transport the sinkhorn cdf and quantile operator"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "object detection with deep learning a review"
    - "convex constrained sparse additive modeling and its extensions"
    - "ldmnet low dimensional manifold regularized neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-5 (induced by source paper "ap perf incorporating generic performance metrics in differentiable learning"):
  with members:
    - "training deep neural networks via direct loss minimization"
    - "julia a fresh approach to numerical computing"
    - "on the consistency of ordinal regression methods"

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)