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: "low rank matrix completion for topological interference management by riemannian pursuit"
  Abstract: In this paper, we present a flexible low-rank matrix completion (LRMC) approach for topological interference management (TIM) in the partially connected K-user interference channel. No channel state information (CSI) is required at the transmitters except the network topology information. The previous attempt on the TIM problem is mainly based on its equivalence to the index coding problem, but so far only a few index coding problems have been solved. In contrast, in this paper, we present an algorithmic approach to investigate the achievable degrees-of-freedom (DoFs) by recasting the TIM problem as an LRMC problem. Unfortunately, the resulting LRMC problem is known to be NP-hard, and the main contribution of this paper is to propose a Riemannian pursuit (RP) framework to detect the rank of the matrix to be recovered by iteratively increasing the rank. This algorithm solves a sequence of fixed-rank matrix completion problems. To address the convergence issues in the existing fixed-rank optimization methods, the quotient manifold geometry of the search space of fixed-rank matrices is exploited via Riemannian optimization. By further exploiting the structure of the low-rank matrix varieties, i.e., the closure of the set of fixed- rank matrices, we develop an efficient rank increasing strategy to find good initial points in the procedure of rank pursuit. Simulation results demonstrate that the proposed RP algorithm achieves a faster convergence rate and higher achievable DoFs for the TIM problem compared with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "nips 2016 tutorial generative adversarial networks"
    - "low rank matrix completion for mobile edge caching in fog ran via riemannian optimization"

Connected through hyperedge HE-2 (induced by source paper "federated learning via over the air computation"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "federated learning with non iid data"

Connected through hyperedge HE-3 (induced by source paper "nonconvex demixing from bilinear measurements"):
  with members:
    - "blind demixing for low latency communication"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-4 (induced by source paper "generalized low rank optimization for topological cooperation in ultra dense networks"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "feasibility of interference alignment for the mimo interference channel"
    - "topological interference management with transmitter cooperation"
    - "topological interference management through index coding"

Connected through hyperedge HE-5 (induced by source paper "blind demixing for low latency communication"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "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"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "large scale convex optimization for ultra dense cloud ran"

Connected through hyperedge HE-6 (induced by source paper "data shuffling in wireless distributed computing via low rank optimization"):
  with members:
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "a scalable framework for wireless distributed computing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "feasibility of interference alignment for the mimo interference channel"

Connected through hyperedge HE-7 (induced by source paper "an ialm ica based anti jamming ds cdma receiver for lms systems"):
  with members:
    - "reduced dimension multiuser detection"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "generalized sparse and low rank optimization for ultra dense networks"):
  with members:
    - "coding for distributed fog computing"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "sparse signal processing concepts for efficient 5g system design"
    - "topological interference management through index coding"
    - "living on the edge the role of proactive caching in 5g wireless 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.IT(Information Theory)