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 gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
  Abstract: We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA). We consider an oracle model where, given a symmetric matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$ \emph{exact} queries of the form $w^{(i)} = Mv^{(i)}$ for $i \in \{1,\dots,T\}$, where $v^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{v^{(j)},w^{(j)}\}_{1 \le j \le i-1}$. We show that for a small constant $\epsilon$, any adaptive, randomized algorithm which can find a unit vector $\widehat{v}$ for which $\widehat{v}^{\top}M\widehat{v} \ge (1-\epsilon)\|M\|$, with even small probability, must make $T = \Omega(\log d)$ queries. In addition to settling a widely-held folk conjecture, this bound demonstrates a fundamental gap between convex optimization and "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension. Our argument proceeds via a reduction to estimating the rank-one spike in a deformed Wigner model. We establish lower bounds for this model by developing a "truncated" analogue of the $\chi^2$ Bayes-risk lower bound of Chen et al.

Connected through hyperedge HE-1 (induced by source paper "the complexity of finding stationary points with stochastic gradient descent"):
  with members:
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "escaping saddles with stochastic gradients"):
  with members:
    - "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"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sub sampled cubic regularization for non convex optimization"
    - "how to escape saddle points efficiently"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "saving gradient and negative curvature computations finding local minima more efficiently"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "sub sampled cubic regularization for non convex optimization"
    - "how to escape saddle points efficiently"
    - "faster eigenvector computation via shift and invert preconditioning"

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)