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: "fast computation of moore penrose inverse matrices"
  Abstract: Many neural learning algorithms require to solve large least square systems in order to obtain synaptic weights. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors of synaptic weights, which contribute to the regularization of the input-output mapping. It is thus of interest to develop fast and accurate algorithms for computing Moore-Penrose inverse matrices. In this paper, an algorithm based on a full rank Cholesky factorization is proposed. The resulting pseudoinverse matrices are similar to those provided by other algorithms. However the computation time is substantially shorter, particularly for large systems.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous multilayer generalized operational perceptron"):
  with members:
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "layer wise training of deep networks using kernel similarity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "progressive learning for systematic design of large neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "cupc cuda based parallel pc algorithm for causal structure learning on gpu"):
  with members:
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "causal inference and causal explanation with background knowledge"

Connected through hyperedge HE-3 (induced by source paper "hierarchical clustering supported by reciprocal nearest neighbors"):
  with members:
    - "social structure of facebook networks"
    - "defining and evaluating network communities based on ground truth"
    - "finding community structure based on subgraph similarity"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "low tubal rank tensor completion using alternating minimization"):
  with members:
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "adaptive sampling of rf fingerprints for fine grained indoor localization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "near optimal hybrid processing for massive mimo systems via matrix decomposition"):
  with members:
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "hybrid block diagonalization for massive multiuser mimo systems"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "subspace estimation and decomposition for hybrid analog digital millimetre wave mimo systems"
    - "on the convergence of massive mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-6 (induced by source paper "dense linear algebra over finite fields the fflas and ffpack packages"):
  with members:
    - "q adic transform revisited"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"
    - "efficient computation of the characteristic polynomial"

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.NE(Neural and Evolutionary Computing)