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 algorithms for the geometric transportation problem"
  Abstract: Let $R$ and $B$ be two point sets in $\mathbb{R}^d$, with $|R|+ |B| = n$ and where $d$ is a constant. Next, let $\lambda : R \cup B \to \mathbb{N}$ such that $\sum_{r \in R } \lambda(r) = \sum_{b \in B} \lambda(b)$ be demand functions over $R$ and $B$. Let $\|\cdot\|$ be a suitable distance function such as the $L_p$ distance. The transportation problem asks to find a map $\tau : R \times B \to \mathbb{N}$ such that $\sum_{b \in B}\tau(r,b) = \lambda(r)$, $\sum_{r \in R}\tau(r,b) = \lambda(b)$, and $\sum_{r \in R, b \in B} \tau(r,b) \|r-b\|$ is minimized. We present three new results for the transportation problem when $\|r-b\|$ is any $L_p$ metric: #R##N#- For any constant $\varepsilon > 0$, an $O(n^{1+\varepsilon})$ expected time randomized algorithm that returns a transportation map with expected cost $O(\log^2(1/\varepsilon))$ times the optimal cost. #R##N#- For any $\varepsilon > 0$, a $(1+\varepsilon)$-approximation in $O(n^{3/2}\varepsilon^{-d} \operatorname{polylog}(U) \operatorname{polylog}(n))$ time, where $U = \max_{p\in R\cup B} \lambda(p)$. #R##N#- An exact strongly polynomial $O(n^2 \operatorname{polylog}n)$ time algorithm, for $d = 2$.

Connected through hyperedge HE-1 (induced by source paper "conditional hardness of earth mover distance"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "parallel algorithms for geometric graph problems"

Connected through hyperedge HE-2 (induced by source paper "a weighted approach to the maximum cardinality bipartite matching problem with applications in geometric settings"):
  with members:
    - "a faster algorithm for minimum cost bipartite matching in minor free graphs"
    - "the weighted matching approach to maximum cardinality matching"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "multiple source multiple sink maximum flow in directed planar graphs in near linear time"

Connected through hyperedge HE-3 (induced by source paper "efficient algorithms for geometric partial matching"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"
    - "scaling algorithms for weighted matching in general graphs"
    - "navigating central path with electrical flows from flows to matchings and back"

Connected through hyperedge HE-4 (induced by source paper "a near linear time approximation scheme for geometric transportation with real supplies"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "parallel algorithms for geometric graph problems"

Connected through hyperedge HE-5 (induced by source paper "a near linear time approximation scheme for geometric transportation with arbitrary supplies and spread"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "parallel algorithms for geometric graph problems"

Connected through hyperedge HE-6 (induced by source paper "preconditioning for the geometric transportation problem"):
  with members:
    - "approximating the quadratic transportation metric in near linear time"

Connected through hyperedge HE-7 (induced by source paper "on geometric alignment in low doubling dimension"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "low cost and faster tracking systems using core sets for pose estimation"
    - "exploiting similarities among languages for machine translation"
    - "fast construction of nets in low dimensional metrics and their applications"

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)