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: "faster p norm minimizing flows via smoothed q norm problems"
  Abstract: We present faster high-accuracy algorithms for computing $\ell_p$-norm minimizing flows. On a graph with $m$ edges, our algorithm can compute a $(1+1/\text{poly}(m))$-approximate unweighted $\ell_p$-norm minimizing flow with $pm^{1+\frac{1}{p-1}+o(1)}$ operations, for any $p \ge 2,$ giving the best bound for all $p\gtrsim 5.24.$ Combined with the algorithm from the work of Adil et al. (SODA '19), we can now compute such flows for any $2\le p\le m^{o(1)}$ in time at most $O(m^{1.24}).$ In comparison, the previous best running time was $\Omega(m^{1.33})$ for large constant $p.$ For $p\sim\delta^{-1}\log m,$ our algorithm computes a $(1+\delta)$-approximate maximum flow on undirected graphs using $m^{1+o(1)}\delta^{-1}$ operations, matching the current best bound, albeit only for unit-capacity graphs. #R##N#We also give an algorithm for solving general $\ell_{p}$-norm regression problems for large $p.$ Our algorithm makes $pm^{\frac{1}{3}+o(1)}\log^2(1/\varepsilon)$ calls to a linear solver. This gives the first high-accuracy algorithm for computing weighted $\ell_{p}$-norm minimizing flows that runs in time $o(m^{1.5})$ for some $p=m^{\Omega(1)}.$ #R##N#Our key technical contribution is to show that smoothed $\ell_p$-norm problems introduced by Adil et al., are interreducible for different values of $p.$ No such reduction is known for standard $\ell_p$-norm problems.

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"
    - "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"
    - "solving linear programs in the current matrix multiplication time"

Connected through hyperedge HE-2 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs"):
  with members:
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"
    - "computing maximum flow with augmenting electrical flows"
    - "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 "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"):
  with members:
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "distributed edge connectivity in sublinear time"
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "flows in almost linear time via adaptive preconditioning"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "short cycles via low diameter decompositions"

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)