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: "recovering the optimal solution by dual random projection"
  Abstract: Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recover the optimal solution to the original optimization problem in the high-dimensional space based on the solution learned from the subspace spanned by random projections. We present a simple algorithm, termed Dual Random Projection, that uses the dual solution of the low-dimensional optimization problem to recover the optimal solution to the original problem. Our theoretical analysis shows that with a high probability, the proposed algorithm is able to accurately recover the optimal solution to the original problem, provided that the data matrix is of low rank or can be well approximated by a low rank matrix.

Connected through hyperedge HE-1 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "the non uniform k center problem"
    - "adversarial machine learning at scale"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "random projections for linear support vector machines"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-2 (induced by source paper "improved subsampled randomized hadamard transform for linear svm"):
  with members:
    - "faster neural network training with approximate tensor operations"
    - "fully understanding the hashing trick"
    - "provable deterministic leverage score sampling"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "random projections for k means clustering"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "stable sparse subspace embedding for dimensionality reduction"):
  with members:
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "random projections for k means clustering"
    - "sparser johnson lindenstrauss transforms"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-4 (induced by source paper "distance geometry and data science"):
  with members:
    - "global optimality conditions for deep neural networks"
    - "theoretical analysis of the k means algorithm a survey"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "faster sgd using sketched conditioning"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "randomized sketches of convex programs with sharp guarantees"
    - "mini batch primal and dual methods for svms"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (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"
    - "theory of dual sparse regularized randomized reduction"
    - "sketching as a tool for numerical linear algebra"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "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-7 (induced by source paper "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"):
  with members:
    - "importance sampling for minibatches"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"
    - "communication complexity of distributed convex learning and optimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "scalable kernel methods via doubly stochastic gradients"
    - "semi stochastic coordinate descent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "theory of dual sparse regularized randomized reduction"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "is margin preserved after random projection"
    - "one bit compressed sensing by linear programming"
    - "a reliable effective terascale linear learning system"
    - "randomized algorithms for matrices and data"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

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)