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: "robust and scalable column row sampling from corrupted big data"
  Abstract: Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "iterative projection and matching finding structure preserving representatives and its application to computer vision"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "deep bayesian active learning with image data"
    - "subsampling for ridge regression via regularized volume sampling"
    - "video summarization with long short term memory"

Connected through hyperedge HE-2 (induced by source paper "spatial random sampling a structure preserving data sketching tool"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "greedy column subset selection for large scale data sets"
    - "optimal column based low rank matrix reconstruction"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-3 (induced by source paper "a survey on data processing methods and cloud computation"):
  with members:
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "big data analytics on traditional hpc infrastructure using two level storage"
    - "decentralized computation offloading game for mobile cloud computing"
    - "a speculative parallel dfa membership test for multicore simd and cloud computing environments"

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)