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: "complete dictionary recovery over the sphere i overview and the geometric picture"
  Abstract: We consider the problem of recovering a complete (i.e., square and invertible) matrix    $ A_{0}$   , from    $ Y \in \mathbb R ^{n \times p}$    with    $ Y = A_{0} X_{0}$   , provided    $ X_{0}$    is sufficiently sparse. This recovery problem is central to theoretical understanding of dictionary learning, which seeks a sparse representation for a collection of input signals and finds numerous applications in modern signal processing and machine learning. We give the first efficient algorithm that provably recovers    $ A_{0}$    when    $ X_{0}$    has    $O \left ({ n }\right )$    nonzeros per column, under suitable probability model for    $ X_{0}$   . In contrast, prior results based on efficient algorithms either only guarantee recovery when    $ X_{0}$    has    $O(\sqrt {n})$    zeros per column, or require multiple rounds of semidefinite programming relaxation to work when    $ X_{0}$    has    $O(n)$    nonzeros per column. Our algorithmic pipeline centers around solving a certain nonconvex optimization problem with a spherical constraint. In this paper, we provide a geometric characterization of the objective landscape. In particular, we show that the problem is highly structured with high probability: 1) there are no “spurious” local minimizers and 2) around all saddle points the objective has a negative directional curvature. This distinctive structure makes the problem amenable to efficient optimization algorithms. In a companion paper, we design a second-order trust-region algorithm over the sphere that provably converges to a local minimizer from arbitrary initializations, despite the presence of saddle points.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "hyperplane clustering via dual principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "complete dictionary learning via ell_p norm maximization"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "subgradient descent learns orthogonal dictionaries"
    - "exact recovery of sparsely used dictionaries"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "analysis of the optimization landscapes for overcomplete representation learning"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "subgradient descent learns orthogonal dictionaries"
    - "on the optimization landscape of tensor decompositions"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "provable representation learning for imitation learning via bi level optimization"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "batch policy learning under constraints"
    - "meta learning with implicit gradients"
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"

Connected through hyperedge HE-5 (induced by source paper "dynamics of deep neural networks and neural tangent hierarchy"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "elimination of all bad local minima in deep learning"
    - "on the power and limitations of random features for understanding neural networks"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-6 (induced by source paper "leader stochastic gradient descent for distributed training of deep learning models"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "understanding the loss surface of neural networks for binary classification"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "integrated model batch and domain parallelism in training neural networks"
    - "parle parallelizing stochastic gradient descent"
    - "scaling sgd batch size to 32k for imagenet training"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-7 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points"):
  with members:
    - "finding local minima via stochastic nested variance reduction"
    - "escaping saddles with stochastic gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-8 (induced by source paper "global optimality guarantees for policy gradient methods"):
  with members:
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "global convergence of policy gradient methods for linearized control problems"
    - "parameter space noise for exploration"
    - "towards generalization and simplicity in continuous control"
    - "how to escape saddle points efficiently"
    - "proximal policy optimization algorithms"
    - "matrix completion has no spurious local minimum"
    - "high dimensional continuous control using generalized advantage estimation"

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.IT(Information Theory)