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: "adaptive sampling of rf fingerprints for fine grained indoor localization"
  Abstract: Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promising approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database #N# a priori #N# for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a #N# tensor #N# and use tensor algebraic methods for an #N# adaptive #N# tubal-sampling of this fingerprint space. In particular, using a recently proposed tensor algebraic framework in #N# [1] #N# , we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation, the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions, we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by #N#   $71$     #N# percent for the high SNR case and #N#  $55$      #N# percent for the low SNR case.

Connected through hyperedge HE-1 (induced by source paper "truncated nuclear norm regularization for low rank tensor completion"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "tensor generative adversarial network with two dimensional sparse coding application to real time indoor localization"):
  with members:
    - "efficient two dimensional sparse coding using tensor linear combination"
    - "fourth order tensors with multidimensional discrete transforms"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "is the deconvolution layer the same as a convolutional layer"
    - "an ls decomposition approach for robust data recovery in wireless sensor networks"
    - "deep networks for image super resolution with sparse prior"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-3 (induced by source paper "low tubal rank tensor completion using alternating minimization"):
  with members:
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "tensor ring decomposition"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "fast matrix completion without the condition number"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "the noisy power method a meta algorithm with applications"
    - "understanding alternating minimization for matrix completion"

Connected through hyperedge HE-4 (induced by source paper "exact tensor completion using t svd"):
  with members:
    - "incoherence optimal matrix completion"
    - "clustering multi way data a novel algebraic approach"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "a new convex relaxation for tensor completion"
    - "the power and arnoldi methods in an algebra of circulants"
    - "recovering low rank matrices from few coefficients in any basis"
    - "note on sampling without replacing from a finite collection of matrices"

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)