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: "exponential convergence of testing error for stochastic gradient methods"
  Abstract: We consider binary classification problems with positive definite kernels and square loss, and study the convergence rates of stochastic gradient methods. We show that while the excess testing loss (squared loss) converges slowly to zero as the number of observations (and thus iterations) goes to infinity, the testing error (classification error) converges exponentially fast if low-noise conditions are assumed.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "quantifying learning guarantees for convex but inconsistent surrogates"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "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-3 (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"
    - "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"

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)