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 "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.

Includes members:
  Member "a consistent regularization approach for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a structured prediction approach for conditional meta learning")
  Member "training deep neural networks via direct loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing rank based metrics with blackbox differentiation")
  Member "simultaneous model selection and optimization through parameter free stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive online learning with varying norms")
  Member "on the consistency of ordinal regression methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")
  Member "convex calibration dimension for multiclass loss matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an embedding framework for consistent polyhedral surrogates")
  Member "surrogate regret bounds for bipartite ranking via strongly proper losses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "risk bounds for low cost bipartite ranking")

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)