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 structured prediction theory with calibrated convex surrogate losses"
  Abstract: We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called "calibration function" relating the excess surrogate risk to the actual risk. In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity. As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for general structured prediction.

Connected through hyperedge HE-1 (induced by source paper "strategic prediction with latent aggregative games"):
  with members:
    - "the mechanics of n player differentiable games"
    - "learning to speed up structured output prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "learning with opponent learning awareness"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-2 (induced by source paper "a general theory for structured prediction with smooth convex surrogates"):
  with members:
    - "sharp analysis of learning with discrete losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "consistent algorithms for multiclass classification with a reject option"
    - "on the consistency of ordinal regression methods"
    - "exponential family graph matching and ranking"

Connected through hyperedge HE-3 (induced by source paper "an embedding framework for consistent polyhedral surrogates"):
  with members:
    - "on the consistency of top k surrogate losses"
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "convex calibration dimension for multiclass loss matrices"
    - "general truthfulness characterizations via convex analysis"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-4 (induced by source paper "leveraging low rank relations between surrogate tasks in structured prediction"):
  with members:
    - "consistent multitask learning with nonlinear output relations"
    - "a consistent regularization approach for structured prediction"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-5 (induced by source paper "calibrated surrogate maximization of linear fractional utility in binary classification"):
  with members:
    - "beyond convexity stochastic quasi convex optimization"
    - "on the consistency of auc pairwise 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 "quantifying learning guarantees for convex but inconsistent surrogates"):
  with members:
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "on the consistency of ordinal regression methods"
    - "convex calibration dimension for multiclass loss matrices"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "minimizing the misclassification error rate using a surrogate convex loss"

Connected through hyperedge HE-7 (induced by source paper "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"):
  with members:
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "train faster generalize better stability of stochastic gradient descent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "parametric adversarial divergences are good task losses for generative modeling"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "mcgan mean and covariance feature matching gan"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "approximation and convergence properties of generative adversarial learning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"

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)