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: "nonconvex demixing from bilinear measurements"
  Abstract: We consider the problem of demixing a sequence of source signals from the sum of noisy bilinear measurements. It is a generalized mathematical model for blind demixing with blind deconvolution, which is prevalent across the areas of dictionary learning, image processing, and communications. However, state-of- the-art convex methods for blind demixing via semidefinite programming are computationally infeasible for large-scale problems. Although the existing nonconvex algorithms are able to address the scaling issue, they normally require proper regularization to establish optimality guarantees. The additional regularization yields tedious algorithmic parameters and pessimistic convergence rates with conservative step sizes. To address the limitations of existing methods, we thus develop a provable nonconvex demixing procedure viaWirtinger flow, much like vanilla gradient descent, to harness the benefits of regularization-free fast convergence rate with aggressive step size and computational optimality guarantees. This is achieved by exploiting the benign geometry of the blind demixing problem, thereby revealing that Wirtinger flow enforces the regularization-free iterates in the region of strong convexity and qualified level of smoothness, where the step size can be chosen aggressively.

Connected through hyperedge HE-1 (induced by source paper "learning one hidden layer neural networks via provable gradient descent with random initialization"):
  with members:
    - "the global optimization geometry of shallow linear neural networks"
    - "nonconvex matrix factorization from rank one measurements"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"

Connected through hyperedge HE-2 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "over the air computation via intelligent reflecting surfaces"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "scheduling policies for federated learning in wireless networks"
    - "the roadmap to 6g ai empowered wireless networks"
    - "blind demixing for low latency communication"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"

Connected through hyperedge HE-3 (induced by source paper "blind demixing for low latency communication"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "painless breakups efficient demixing of low rank matrices"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "large scale convex optimization for ultra dense cloud ran"

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)