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: "on the complexity of learning with kernels"
  Abstract: A well-recognized limitation of kernel learning is the requirement to handle a kernel matrix, whose size is quadratic in the number of training examples. Many methods have been proposed to reduce this computational cost, mostly by using a subset of the kernel matrix entries, or some form of low-rank matrix approximation, or a random projection method. In this paper, we study lower bounds on the error attainable by such methods as a function of the number of entries observed in the kernel matrix or the rank of an approximate kernel matrix. We show that there are kernel learning problems where no such method will lead to non-trivial computational savings. Our results also quantify how the problem difficulty depends on parameters such as the nature of the loss function, the regularization parameter, the norm of the desired predictor, and the kernel matrix rank. Our results also suggest cases where more efficient kernel learning might be possible.

Connected through hyperedge HE-1 (induced by source paper "correlation clustering with adaptive similarity queries"):
  with members:
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "predicting positive and negative links with noisy queries theory practice"
    - "approximate correlation clustering using same cluster queries"
    - "community recovery in graphs with locality"
    - "a survey of signed network mining in social media"
    - "local correlation clustering"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "tight kernel query complexity of kernel ridge regression and kernel k means clustering"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "on learning mixtures of well separated gaussians"
    - "recursive sampling for the nystr om method"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "sharp analysis of low rank kernel matrix approximations"
    - "adaptively learning the crowd kernel"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "logistic regression the importance of being improper"
    - "large scale kernel learning using block coordinate descent"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates in statistical and online learning"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-4 (induced by source paper "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"):
  with members:
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-5 (induced by source paper "learning infinite layer networks without the kernel trick"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "steps toward deep kernel methods from infinite neural networks"
    - "the sample complexity of learning linear predictors with the squared loss"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"
    - "breaking the curse of dimensionality with convex neural networks"

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)