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: "theory of dual sparse regularized randomized reduction"
  Abstract: In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashing), for large-scale high-dimensional classification. Previous theoretical results on randomized reduction methods hinge on strong assumptions about the data, e.g., low rank of the data matrix or a large separable margin of classification, which hinder their applications in broad domains. To address these limitations, we propose dual-sparse regularized randomized reduction methods that introduce a sparse regularizer into the reduced dual problem. Under a mild condition that the original dual solution is a (nearly) sparse vector, we show that the resulting dual solution is close to the original dual solution and concentrates on its support set. In numerical experiments, we present an empirical study to support the analysis and we also present a novel application of the dual-sparse regularized randomized reduction methods to reducing the communication cost of distributed learning from large-scale high-dimensional data.

Connected through hyperedge HE-1 (induced by source paper "sketching linear classifiers over data streams"):
  with members:
    - "introduction to online convex optimization"
    - "incremental skip gram model with negative sampling"
    - "algorithmic decision making and the cost of fairness"
    - "federated multi task learning"
    - "macrobase prioritizing attention in fast data"
    - "why should i trust you explaining the predictions of any classifier"
    - "heavy hitters via cluster preserving clustering"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-2 (induced by source paper "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"
    - "sketching as a tool for numerical linear algebra"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "recovering the optimal solution by dual random projection"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-3 (induced by source paper "efficient non oblivious randomized reduction for risk minimization with improved excess risk guarantee"):
  with members:
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "sharp analysis of low rank kernel matrix approximations"
    - "is margin preserved after random projection"

Connected through hyperedge HE-4 (induced by source paper "sparse learning for large scale and high dimensional data a randomized convex concave optimization approach"):
  with members:
    - "recovering the optimal solution by dual random projection"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "is margin preserved after random projection"
    - "one bit compressed sensing by linear programming"
    - "optimization with sparsity inducing penalties"
    - "randomized algorithms for matrices and data"

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)