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: "computing maximum flow with augmenting electrical flows"
  Abstract: We present an $\tilde{O}\left(m^{\frac{10}{7}}U^{\frac{1}{7}}\right)$-time algorithm for the maximum $s$-$t$ flow problem and the minimum $s$-$t$ cut problem in directed graphs with $m$ arcs and largest integer capacity $U$. This matches the running time of the $\tilde{O}\left((mU)^{\frac{10}{7}}\right)$-time algorithm of M\k{a}dry (FOCS 2013) in the unit-capacity case, and improves over it, as well as over the $\tilde{O}\left(m \sqrt{n} \log U\right)$-time algorithm of Lee and Sidford (FOCS 2014), whenever $U$ is moderately large and the graph is sufficiently sparse. By well-known reductions, this also gives similar running time improvements for the maximum-cardinality bipartite $b$-matching problem. #R##N#One of the advantages of our algorithm is that it is significantly simpler than the ones presented in Madry (FOCS 2013) and Lee and Sidford (FOCS 2014). In particular, these algorithms employ a sophisticated interior-point method framework, while our algorithm is cast directly in the classic augmenting path setting that almost all the combinatorial maximum flow algorithms use. At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting $s$-$t$ flow in the current residual graph and to update the dual solution. We show that by maintain certain careful coupling of these primal and dual solutions we are always guaranteed to make significant progress.

Connected through hyperedge HE-1 (induced by source paper "faster divergence maximization for faster maximum flow"):
  with members:
    - "circulation control for faster minimum cost flow in unit capacity graphs"
    - "solving tall dense linear programs in nearly linear time"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "solving linear programs with sqrt rank linear system solves"
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"

Connected through hyperedge HE-2 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs"):
  with members:
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "faster energy maximization for faster maximum flow"):
  with members:
    - "solving linear programs with sqrt rank linear system solves"
    - "min cost flow in unit capacity planar graphs"
    - "flows in almost linear time via adaptive preconditioning"
    - "solving linear programs in the current matrix multiplication time"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "routing under balance"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-5 (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"
    - "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"
    - "efficient inverse maintenance and faster algorithms for linear programming"

Connected through hyperedge HE-6 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "decentralized random walk based data collection in networks"
    - "sampling random spanning trees faster than matrix multiplication"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "fast generation of random spanning trees and the effective resistance metric"

Connected through hyperedge HE-7 (induced by source paper "electrical flows over spanning trees"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "sampling random spanning trees faster than matrix multiplication"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "graph sparsification by effective resistances"
    - "nearly tight low stretch spanning trees"

Connected through hyperedge HE-8 (induced by source paper "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"):
  with members:
    - "deterministic partially dynamic single source shortest paths in weighted graphs"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "dynamic approximate all pairs shortest paths breaking the o mn barrier and derandomization"
    - "nearly maximum flows in nearly linear time"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"

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.DS(Data Structures and Algorithms)