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: "a consistent regularization approach for structured prediction"
  Abstract: We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed methods. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "a structured prediction approach for conditional meta learning"):
  with members:
    - "meta learning with implicit gradients"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-2 (induced by source paper "structured prediction with partial labelling through the infimum loss"):
  with members:
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "a general theory for structured prediction with smooth convex surrogates"):
  with members:
    - "sharp analysis of learning with discrete losses"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "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-4 (induced by source paper "leveraging low rank relations between surrogate tasks in structured prediction"):
  with members:
    - "consistent multitask learning with nonlinear output relations"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "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 "quantifying learning guarantees for convex but inconsistent surrogates"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "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-6 (induced by source paper "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "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-7 (induced by source paper "sharp analysis of learning with discrete losses"):
  with members:
    - "on the consistency of ordinal regression methods"
    - "convex calibration dimension for multiclass loss matrices"
    - "a theoretical analysis of ndcg type ranking measures"

Connected through hyperedge HE-8 (induced by source paper "structured output learning with abstention application to accurate opinion prediction"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"

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)