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: "towards 1 epsilon approximate flow sparsifiers"
  Abstract: A useful approach to "compress" a large network $G$ is to represent it with a {\em flow-sparsifier}, i.e., a small network $H$ that supports the same flows as $G$, up to a factor $q \geq 1$ called the quality of sparsifier. Specifically, we assume the network $G$ contains a set of $k$ terminals $T$, shared with the network $H$, i.e., $T\subseteq V(G)\cap V(H)$, and we want $H$ to preserve all multicommodity flows that can be routed between the terminals $T$. The challenge is to construct $H$ that is small. #R##N#These questions have received a lot of attention in recent years, leading to some known tradeoffs between the sparsifier's quality $q$ and its size $|V(H)|$. Nevertheless, it remains an outstanding question whether every $G$ admits a flow-sparsifier $H$ with quality $q=1+\epsilon$, or even $q=O(1)$, and size $|V(H)|\leq f(k,\epsilon)$ (in particular, independent of $|V(G)|$ and the edge capacities). Making a first step in this direction, we present new constructions for several scenarios: #R##N#* Our main result is that for quasi-bipartite networks $G$, one can construct a $(1+\epsilon)$-flow-sparsifier of size $\poly(k/\eps)$. In contrast, exact ($q=1$) sparsifiers for this family of networks are known to require size $2^{\Omega(k)}$. #R##N#* For networks $G$ of bounded treewidth $w$, we construct a flow-sparsifier with quality $q=O(\log w / \log\log w)$ and size $O(w\cdot \poly(k))$. #R##N#* For general networks $G$, we construct a {\em sketch} $sk(G)$, that stores all the feasible multicommodity flows up to factor $q=1+\eps$, and its size (storage requirement) is $f(k,\epsilon)$.

Connected through hyperedge HE-1 (induced by source paper "vertex sparsifiers new results from old techniques"):
  with members:
    - "cutting corners cheaply or how to remove steiner points"
    - "approximation algorithms and hardness of the k route cut problem"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "flow cut gaps for integer and fractional multiflows"
    - "interchanging distance and capacity in probabilistic mappings"

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)