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: "is margin preserved after random projection"
  Abstract: Random projections have been applied in many machine learning algorithms. However, whether margin is preserved after random projection is non-trivial and not well studied. In this paper we analyse margin distortion after random projection, and give the conditions of margin preservation for binary classification problems. We also extend our analysis to margin for multiclass problems, and provide theoretical bounds on multiclass margin on the projected data.

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"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"

Connected through hyperedge HE-2 (induced by source paper "compressive hyperspherical energy minimization"):
  with members:
    - "decoupled networks"
    - "group normalization"
    - "learning towards minimum hyperspherical energy"
    - "darts differentiable architecture search"
    - "deep semi random features for nonlinear function approximation"
    - "diversity promoting bayesian learning of latent variable models"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"

Connected through hyperedge HE-3 (induced by source paper "compressed subspace learning based on canonical angle preserving property"):
  with members:
    - "johnson lindenstrauss property implies subspace restricted isometry property"
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "what happens to a manifold under a bi lipschitz map"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "greedy feature selection for subspace clustering"
    - "the restricted isometry property for random block diagonal matrices"

Connected through hyperedge HE-4 (induced by source paper "billion scale network embedding with iterative random projection"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "struc2vec learning node representations from structural identity"
    - "timers error bounded svd restart on dynamic networks"
    - "a survey on network embedding"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "attributed network embedding for learning in a dynamic environment"
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "there and back again a general approach to learning sparse models"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "tensor decompositions for learning latent variable models"
    - "exact recovery of sparsely used dictionaries"
    - "sparser johnson lindenstrauss transforms"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "friction from reflectance deep reflectance codes for predicting physical surface properties from one shot in field reflectance"):
  with members:
    - "reflectance hashing for material recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "material recognition in the wild with the materials in context database"
    - "matconvnet convolutional neural networks for matlab"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (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"
    - "theory of dual sparse regularized randomized reduction"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "theory of dual sparse regularized randomized reduction"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "recovering the optimal solution by dual 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)