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: "fast provable algorithms for isotonic regression in all ell_ p norms"
  Abstract: Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $||x-y||,$ for a specified norm. This paper gives improved algorithms for computing the Isotonic Regression for all weighted $\ell_{p}$-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.

Connected through hyperedge HE-1 (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-2 (induced by source paper "efficient second order shape constrained function fitting"):
  with members:
    - "efficient algorithms for non convex isotonic regression through submodular optimization"
    - "a deterministic algorithm for fitting a step function to a weighted point set"
    - "efficient learning of generalized linear and single index models with isotonic regression"
    - "lipschitz unimodal and isotonic regression on paths and trees"

Connected through hyperedge HE-3 (induced by source paper "clustered monotone transforms for rating factorization"):
  with members:
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "neural network matrix factorization"
    - "learning to rank with bregman divergences and monotone retargeting"
    - "social knowledge based recommender system application to the movies domain"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-4 (induced by source paper "approximate gaussian elimination for laplacians fast sparse and simple"):
  with members:
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "uniform sampling for matrix approximation"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "faster approximate multicommodity flow using quadratically coupled flows"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"

Connected through hyperedge HE-5 (induced by source paper "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "sum of squares lower bounds for planted clique"
    - "on the hardness of signaling"
    - "noisy sorting without resampling"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of cycle toggling based laplacian solvers"):
  with members:
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "runtime guarantees for regression problems"
    - "approaching optimality for solving sdd systems"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-7 (induced by source paper "online isotonic regression"):
  with members:
    - "large scale probabilistic predictors with and without guarantees of validity"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "predicting accurate probabilities with a ranking loss"
    - "efficient learning of generalized linear and single index models with isotonic regression"

Connected through hyperedge HE-8 (induced by source paper "fast algorithms for segmented regression"):
  with members:
    - "sample optimal density estimation in nearly linear time"

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)