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: "near optimal entrywise sampling for data matrices"
  Abstract: We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$. For large $m \times n$ matrices, such that $n \gg m$ (for example, representing $n$ observations over $m$ attributes) we give sampling distributions that exhibit four important properties. First, they have closed forms computable from minimal information regarding $A$. Second, they allow sketching of matrices whose non-zeros are presented to the algorithm in arbitrary order as a stream, with $O(1)$ computation per non-zero. Third, the resulting sketch matrices are not only sparse, but their non-zero entries are highly compressible. Lastly, and most importantly, under mild assumptions, our distributions are provably competitive with the optimal offline distribution. Note that the probabilities in the optimal offline distribution may be complex functions of all the entries in the matrix. Therefore, regardless of computational complexity, the optimal distribution might be impossible to compute in the streaming model.

Connected through hyperedge HE-1 (induced by source paper "temporal network sampling"):
  with members:
    - "from community to role based graph embeddings"
    - "temporal network representation learning"
    - "dynamic joint variational graph autoencoders"
    - "influential user subscription on time decaying social streams"
    - "explanations for temporal recommendations"
    - "sampling for approximate bipartite network projection"
    - "on sampling from massive graph streams"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "network shrinkage estimation"):
  with members:
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "three hypergraph eigenvector centralities"
    - "on sampling from massive graph streams"
    - "representing higher order dependencies in networks"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "triadic measures on graphs the power of wedge sampling"

Connected through hyperedge HE-3 (induced by source paper "exploiting numerical sparsity for efficient learning faster eigenvector computation and regression"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "a note on randomized element wise matrix sparsification"

Connected through hyperedge HE-4 (induced by source paper "robust frequent directions with application in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "co occuring directions sketching for approximate matrix multiply"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "sketching as a tool for numerical linear algebra"
    - "provable deterministic leverage score sampling"

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)