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: "deterministic and probabilistic conditions for finite completability of low rank tensor"
  Abstract: We investigate the fundamental conditions on the sampling pattern, i.e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank. In order to find the deterministic necessary and sufficient conditions, we propose an algebraic geometric analysis on the Tucker manifold, which allows us to incorporate multiple rank components in the proposed analysis in contrast with the conventional geometric approaches on the Grassmannian manifold. This analysis characterizes the algebraic independence of a set of polynomials defined based on the sampling pattern, which is closely related to finite completion. Probabilistic conditions are then studied and a lower bound on the sampling probability is given, which guarantees that the proposed deterministic conditions on the sampling patterns for finite completability hold with high probability. Furthermore, using the proposed geometric approach for finite completability, we propose a sufficient condition on the sampling pattern that ensures there exists exactly one completion for the sampled tensor.

Connected through hyperedge HE-1 (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"
    - "rank determination for low rank data completion"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "identity mappings in deep residual networks"
    - "earliness aware deep convolutional networks for early time series classification"

Connected through hyperedge HE-2 (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"
    - "rank determination for low rank data completion"
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train 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-3 (induced by source paper "tensor train neighborhood preserving embedding"):
  with members:
    - "wide compression tensor ring nets"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "compressing recurrent neural network with tensor train"
    - "efficient low rank tensor ring completion"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "tensorizing neural networks"

Connected through hyperedge HE-4 (induced by source paper "deterministic and probabilistic conditions for finite completability of low rank multi view data"):
  with members:
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"
    - "low tubal rank tensor completion using alternating minimization"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "a survey on multi view learning"
    - "understanding alternating minimization for matrix completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "multi view learning in the presence of view disagreement"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-5 (induced by source paper "efficient low rank tensor ring completion"):
  with members:
    - "efficient tensor completion low rank tensor train"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "tensor networks for big data analytics and large scale optimization problems"

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.NA(Numerical Analysis)