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: "distribution specific hardness of learning neural networks"
  Abstract: Although neural networks are routinely and successfully trained in practice using simple gradient-based methods, most existing theoretical results are negative, showing that learning such networks is difficult, in a worst-case sense over all data distributions. In this paper, we take a more nuanced view, and consider whether specific assumptions on the "niceness" of the input distribution, or "niceness" of the target function (e.g. in terms of smoothness, non-degeneracy, incoherence, random choice of parameters etc.), are sufficient to guarantee learnability using gradient-based methods. We provide evidence that neither class of assumptions alone is sufficient: On the one hand, for any member of a class of "nice" target functions, there are difficult input distributions. On the other hand, we identify a family of simple target functions, which are difficult to learn even if the input distribution is "nice". To prove our results, we develop some tools which may be of independent interest, such as extending Fourier-based hardness techniques developed in the context of statistical queries \cite{blum1994weakly}, from the Boolean cube to Euclidean space and to more general classes of functions.

Connected through hyperedge HE-1 (induced by source paper "poly time universality and limitations of deep learning"):
  with members:
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "understanding deep learning requires rethinking generalization"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-3 (induced by source paper "tight sample complexity of learning one hidden layer convolutional neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-4 (induced by source paper "on the power and limitations of random features for understanding neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "why do larger models generalize better a theoretical perspective via the xor problem"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"

Connected through hyperedge HE-5 (induced by source paper "spurious local minima of shallow relu networks conform with the symmetry of the target model"):
  with members:
    - "weight space symmetry in deep networks gives rise to permutation saddles connected by equal loss valleys across the loss landscape"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "skip connections eliminate singularities"

Connected through hyperedge HE-6 (induced by source paper "time accuracy tradeoffs for learning a relu with respect to gaussian marginals"):
  with members:
    - "complexity of training relu neural networks"
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "efficient active learning of sparse halfspaces"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "learning geometric concepts with nasty noise"

Connected through hyperedge HE-7 (induced by source paper "depth separations in neural networks what is actually being separated"):
  with members:
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "failures of gradient based deep learning"
    - "depth separation for neural networks"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-8 (induced by source paper "learning and generalization in overparameterized neural networks going beyond two layers"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "can sgd learn recurrent neural networks with provable generalization"
    - "what can resnet learn efficiently going beyond kernels"
    - "learning two layer rectified neural networks in polynomial time"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent 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)