human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on deterministic sampling patterns for robust low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "fundamental conditions for low cp rank tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "rank determination for low rank data completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "low tubal rank tensor completion using alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor completion for multiway visual data")
  Member "tensor completion by alternating minimization under the tensor train tt model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low rank tensor grid for image completion")
  Member "a new convex relaxation for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "the algebraic combinatorial approach for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "phase retrieval via matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

Question: Please tell me which class the source paper 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)