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: "algorithms for lipschitz learning on graphs"
  Abstract: We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices. The extension we compute is the absolutely minimal Lipschitz extension, and is the limit for large $p$ of $p$-Laplacian regularization. We present an algorithm that computes a minimal Lipschitz extension in expected linear time, and an algorithm that computes an absolutely minimal Lipschitz extension in expected time $\widetilde{O} (m n)$. The latter algorithm has variants that seem to run much faster in practice. These extensions are particularly amenable to regularization: we can perform $l_{0}$-regularization on the given values in polynomial time and $l_{1}$-regularization on the initial function values and on graph edge weights in time $\widetilde{O} (m^{3/2})$.

Connected through hyperedge HE-1 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "iterative refinement for ell_p norm regression"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning in network structured data via total variation minimization"):
  with members:
    - "sparse recovery over graph incidence matrices"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "network lasso clustering and optimization in large graphs"
    - "signal recovery on graphs variation minimization"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "graph based semi supervised active learning for edge flows"):
  with members:
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"
    - "hodge laplacians on graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-5 (induced by source paper "fast provably convergent irls algorithm for p norm linear regression"):
  with members:
    - "flows in almost linear time via adaptive preconditioning"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "irls and slime mold equivalence and convergence"
    - "on a natural dynamics for linear programming"

Connected through hyperedge HE-6 (induced by source paper "lipschitz learning for signal recovery"):
  with members:
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"

Connected through hyperedge HE-7 (induced by source paper "lipschitz regularized deep neural networks generalize and are adversarially robust"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "spectral normalization for generative adversarial networks"
    - "improved robustness to adversarial examples using lipschitz regularization of the loss"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "a surprising linear relationship predicts test performance in deep networks"

Connected through hyperedge HE-8 (induced by source paper "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"):
  with members:
    - "non convex matrix completion against a semi random adversary"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "sampling random spanning trees faster than matrix multiplication"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "on sketching quadratic forms"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"

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)