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: "optimal learning for multi pass stochastic gradient methods"
  Abstract: We analyze the learning properties of the stochastic gradient method when multiple passes over the data and mini-batches are allowed. In particular, we consider the square loss and show that for a universal step-size choice, the number of passes acts as a regularization parameter, and optimal finite sample bounds can be achieved by early-stopping. Moreover, we show that larger step-sizes are allowed when considering mini-batches. Our analysis is based on a unifying approach, encompassing both batch and stochastic gradient methods as special cases.

Connected through hyperedge HE-1 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "generalization bounds for uniformly stable algorithms"
    - "privacy preserving prediction"
    - "tight analyses for non smooth stochastic gradient descent"
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "data dependent stability of stochastic gradient descent"
    - "subgaussian tail bounds via stability arguments"
    - "concentration bounds for high sensitivity functions through differential privacy"

Connected through hyperedge HE-2 (induced by source paper "implicit regularization of accelerated methods in hilbert spaces"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "generalization bounds of sgld for non convex learning two theoretical viewpoints"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "how to escape saddle points efficiently"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "understanding deep learning requires rethinking generalization"
    - "dynamics of stochastic gradient algorithms"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-4 (induced by source paper "fast and strong convergence of online learning algorithms"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "convergence of unregularized online learning algorithms"
    - "distributed learning with regularized least squares"
    - "unregularized online learning algorithms with general loss functions"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"):
  with members:
    - "data dependent stability of stochastic gradient descent"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "ensemble robustness of deep learning algorithms"
    - "minimizing the maximal loss how and why"
    - "train faster generalize better stability of stochastic gradient descent"

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)