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: "fast rates by transferring from auxiliary hypotheses"
  Abstract: In this work we consider the learning setting where, in addition to the training set, the learner receives a collection of auxiliary hypotheses originating from other tasks. We focus on a broad class of ERM-based linear algorithms that can be instantiated with any non-negative smooth loss function and any strongly convex regularizer. We establish generalization and excess risk bounds, showing that, if the algorithm is fed with a good combination of source hypotheses, generalization happens at the fast rate $$\mathcal {O}(1/m)$$O(1/m) instead of the usual $$\mathcal {O}(1/\sqrt{m})$$O(1/m). On the other hand, if the source hypotheses combination is a misfit for the target task, we recover the usual learning rate. As a byproduct of our study, we also prove a new bound on the Rademacher complexity of the smooth loss class under weaker assumptions compared to previous works.

Connected through hyperedge HE-1 (induced by source paper "hierarchically structured meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "bayesian model agnostic meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "transferring knowledge across learning processes"
    - "meta learning for low resource neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "learning to learn stochastic gradient descent with biased regularization"):
  with members:
    - "surrogate losses for online learning of stepsizes in stochastic non convex optimization"
    - "introduction to online convex optimization"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a pac approach to application specific algorithm selection"
    - "sparse coding for multitask and transfer learning"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "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-3 (induced by source paper "handling concept drift via model reuse"):
  with members:
    - "hybrid forest a concept drift aware data stream mining algorithm"
    - "introduction to online convex optimization"
    - "robust artificial intelligence and robust human organizations"
    - "adaptive online learning in dynamic environments"
    - "learn on source refine on target a model transfer learning framework with random forests"
    - "concept drift adaptation by exploiting historical knowledge"
    - "a vector contraction inequality for rademacher complexities"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"

Connected through hyperedge HE-4 (induced by source paper "data dependent stability of stochastic gradient descent"):
  with members:
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

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)