human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a general theory for structured prediction with smooth convex surrogates"):
  Abstract: In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e.g. logistic regression). The theory relies on a natural characterization of structural properties of the task loss and allows to derive statistical guarantees for many widely used methods in the context of multilabeling, ranking, ordinal regression and graph matching. In particular, we characterize the smooth convex surrogates compatible with a given task loss in terms of a suitable Bregman divergence composed with a link function. This allows to derive tight bounds for the calibration function and to obtain novel results on existing surrogate frameworks for structured prediction such as conditional random fields and quadratic surrogates.

Includes members:
  Member "sharp analysis of learning with discrete losses":
    also connected through other hyperedges:
      (none)
  Member "on structured prediction theory with calibrated convex surrogate losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strategic prediction with latent aggregative games")
  Member "exponential convergence of testing error for stochastic gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantifying learning guarantees for convex but inconsistent surrogates")
  Member "a consistent regularization approach for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a structured prediction approach for conditional meta learning")
  Member "consistent algorithms for multiclass classification with a reject option":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aleatoric and epistemic uncertainty in machine learning a tutorial introduction")
  Member "on the consistency of ordinal regression methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")
  Member "exponential family graph matching and ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving partial assignment problems using random clique complexes")

Question: Please tell me which class the source paper 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)