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: "distributed low rank approximation of implicit functions of a matrix"
  Abstract: We study distributed low rank approximation in which the matrix to be approximated is only implicitly represented across the different servers. For example, each of $s$ servers may have an $n \times d$ matrix $A^t$, and we may be interested in computing a low rank approximation to $A = f(\sum_{t=1}^s A^t)$, where $f$ is a function which is applied entrywise to the matrix $\sum_{t=1}^s A^t$. We show for a wide class of functions $f$ it is possible to efficiently compute a $d \times d$ rank-$k$ projection matrix $P$ for which $\|A - AP\|_F^2 \leq \|A - [A]_k\|_F^2 + \varepsilon \|A\|_F^2$, where $AP$ denotes the projection of $A$ onto the row span of $P$, and $[A]_k$ denotes the best rank-$k$ approximation to $A$ given by the singular value decomposition. The communication cost of our protocols is $d \cdot (sk/\varepsilon)^{O(1)}$, and they succeed with high probability. Our framework allows us to efficiently compute a low rank approximation to an entry-wise softmax, to a Gaussian kernel expansion, and to $M$-Estimators applied entrywise (i.e., forms of robust low rank approximation). We also show that our additive error approximation is best possible, in the sense that any protocol achieving relative error for these problems requires significantly more communication. Finally, we experimentally validate our algorithms on real datasets.

Connected through hyperedge HE-1 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "communication efficient distributed kernel principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-3 (induced by source paper "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "constant factor fpt approximation for capacitated k median"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-4 (induced by source paper "distributed statistical estimation of matrix products with applications"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "worst case optimal algorithms for parallel query processing"
    - "on the complexity of inner product similarity join"
    - "communication efficient distributed kernel principal component analysis"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "skew in parallel query processing"
    - "improved distributed principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "optimal principal component analysis in distributed and streaming models"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "tighter low rank approximation via sampling the leveraged element"
    - "relative errors for deterministic low rank matrix approximations"
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"

Connected through hyperedge HE-6 (induced by source paper "communication lower bounds for statistical estimation problems via a distributed data processing inequality"):
  with members:
    - "optimal principal component analysis in distributed and streaming models"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "improved distributed principal component analysis"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "principal component analysis and higher correlations for distributed data"
    - "tight bounds for distributed functional monitoring"

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.NA(Numerical Analysis)