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 quality of the initial basin in overspecified neural networks"
  Abstract: Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the \emph{geometric} structure of the associated non-convex objective function, in the context of ReLU networks and starting from a random initialization of the network parameters. We identify some conditions under which it becomes more favorable to optimization, in the sense of (i) High probability of initializing at a point from which there is a monotonically decreasing path to a global minimum; and (ii) High probability of initializing at a basin (suitably defined) with a small minimal objective value. A common theme in our results is that such properties are more likely to hold for larger ("overspecified") networks, which accords with some recent empirical and theoretical observations.

Connected through hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

Connected through hyperedge HE-2 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-3 (induced by source paper "generalization error bounds of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "an improved analysis of training over parameterized deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "tight sample complexity of learning one hidden layer convolutional neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-4 (induced by source paper "on connected sublevel sets in deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-5 (induced by source paper "lca loss change allocation for neural network training"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "measuring the intrinsic dimension of objective landscapes"
    - "fix your classifier the marginal value of training the last weight layer"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "visualizing the loss landscape of neural nets"
    - "critical learning periods in deep neural networks"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "landscape connectivity and dropout stability of sgd solutions for over parameterized neural networks"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on connected sublevel sets in deep learning"
    - "the state of sparsity in deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-8 (induced by source paper "trainability and data dependent initialization of over parameterized relu neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the convergence of adam and beyond"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow 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)