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: "rank determination for low rank data completion"
  Abstract: Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper, we consider the scenario where the rank is not given and we aim to approximate the unknown rank based on the location of sampled entries and some given completion. We consider a number of data models, including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor and Tucker tensor. For each of these data models, we provide an upper bound on the rank when an arbitrary low-rank completion is given. We characterize these bounds both deterministically, i.e., with probability one given that the sampling pattern satisfies certain combinatorial properties, and probabilistically, i.e., with high probability given that the sampling probability is above some threshold. Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given. Furthermore, we provide numerical experiments for the case of single-view matrix, where we use nuclear norm minimization to find a low-rank completion of the sampled data and we observe that in most of the cases the proposed upper bound on the rank is equal to the true rank.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "mc 2 a two phase algorithm for leveraged matrix completion"
    - "tighter low rank approximation via sampling the leveraged element"

Connected through hyperedge HE-2 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm"):
  with members:
    - "multi target position and velocity estimation using ofdm communication signals"
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "near optimal sample complexity for convex tensor completion"
    - "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"
    - "frequency selective vandermonde decomposition of toeplitz matrices with applications"

Connected through hyperedge HE-3 (induced by source paper "matrix completion with deterministic pattern a geometric perspective"):
  with members:
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "practical sketching algorithms for low rank matrix approximation"
    - "guaranteed matrix completion via non convex factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "incoherence optimal matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-4 (induced by source paper "wide compression tensor ring nets"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "tensor train neighborhood preserving embedding"
    - "topic compositional neural language model"
    - "tensor train recurrent neural networks for video classification"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "identity mappings in deep residual networks"
    - "earliness aware deep convolutional networks for early time series classification"

Connected through hyperedge HE-5 (induced by source paper "on deterministic sampling patterns for robust low rank matrix completion"):
  with members:
    - "fundamental conditions for low cp rank tensor completion"
    - "deterministic and probabilistic conditions for finite completability of low rank multi view data"
    - "efficient low rank tensor ring completion"
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "low tubal rank tensor completion using alternating minimization"
    - "fast algorithms for robust pca via gradient descent"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-6 (induced by source paper "deterministic and probabilistic conditions for finite completability of low rank tensor"):
  with members:
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "low tubal rank tensor completion using alternating minimization"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "a new convex relaxation for tensor completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "phase retrieval via matrix completion"
    - "high rank matrix completion and subspace clustering with missing data"

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)