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: "fundamental conditions for low cp rank tensor completion"
  Abstract: We consider the problem of low canonical polyadic (CP) rank tensor completion. A completion is a tensor whose entries agree with the observed entries and its rank matches the given CP rank. We analyze the manifold structure corresponding to the tensors with the given rank and define a set of polynomials based on the sampling pattern and CP decomposition. Then, we show that finite completability of the sampled tensor is equivalent to having a certain number of algebraically independent polynomials among the defined polynomials. Our proposed approach results in characterizing the maximum number of algebraically independent polynomials in terms of a simple geometric structure of the sampling pattern, and therefore we obtain the deterministic necessary and sufficient condition on the sampling pattern for finite completability of the sampled tensor. Moreover, assuming that the entries of the tensor are sampled independently with probability $p$ and using the mentioned deterministic analysis, we propose a combinatorial method to derive a lower bound on the sampling probability $p$, or equivalently, the number of sampled entries that guarantees finite completability with high probability. We also show that the existing result for the matrix completion problem can be used to obtain a loose lower bound on the sampling probability $p$. In addition, we obtain deterministic and probabilistic conditions for unique completability. It is seen that the number of samples required for finite or unique completability obtained by the proposed analysis on the CP manifold is orders-of-magnitude lower than that is obtained by the existing analysis on the Grassmannian manifold.

Connected through hyperedge HE-1 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-2 (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"
    - "rank determination for low rank data 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-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"
    - "rank determination for low rank data 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 "on deterministic sampling patterns for robust low rank matrix completion"):
  with members:
    - "deterministic and probabilistic conditions for finite completability of low rank multi view data"
    - "efficient low rank tensor ring completion"
    - "rank determination for low rank data 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-5 (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"
    - "rank determination for low rank data 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)