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: "optimal orthogonal graph drawing with convex bend costs"
  Abstract: Traditionally, the quality of orthogonal planar drawings is quantified by either the total number of bends, or the maximum number of bends per edge. However, this neglects that in typical applications, edges have varying importance. Moreover, as bend minimization over all planar embeddings is NP-hard, most approaches focus on a fixed planar embedding. #R##N#We consider the problem OptimalFlexDraw that is defined as follows. Given a planar graph G on n vertices with maximum degree 4 and for each edge e a cost function cost_e : N_0 --> R defining costs depending on the number of bends on e, compute an orthogonal drawing of G of minimum cost. Note that this optimizes over all planar embeddings of the input graphs, and the cost functions allow fine-grained control on the bends of edges. #R##N#In this generality OptimalFlexDraw is NP-hard. We show that it can be solved efficiently if 1) the cost function of each edge is convex and 2) the first bend on each edge does not cause any cost (which is a condition similar to the positive flexibility for the decision problem FlexDraw). Moreover, we show the existence of an optimal solution with at most three bends per edge except for a single edge per block (maximal biconnected component) with up to four bends. For biconnected graphs we obtain a running time of O(n T_flow(n)), where T_flow(n) denotes the time necessary to compute a minimum-cost flow in a planar flow network with multiple sources and sinks. For connected graphs that are not biconnected we need an additional factor of O(n).

Connected through hyperedge HE-1 (induced by source paper "an spqr tree like embedding representation for upward planarity"):
  with members:
    - "extending upward planar graph drawings"
    - "bend minimum orthogonal drawings in quadratic time"
    - "level planarity transitivity vs even crossings"
    - "simultaneous embedding edge orderings relative positions cutvertices"
    - "orthogonal graph drawing with inflexible edges"
    - "planar embeddings with small and uniform faces"
    - "a kuratowski type theorem for planarity of partially embedded graphs"
    - "simultaneous embedding of planar graphs"

Connected through hyperedge HE-2 (induced by source paper "bend minimum orthogonal drawings in quadratic time"):
  with members:
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"

Connected through hyperedge HE-3 (induced by source paper "towards a topology shape metrics framework for ortho radial drawings"):
  with members:
    - "orthogonal graph drawing with inflexible edges"
    - "complexity of higher degree orthogonal graph embedding in the kandinsky model"

Connected through hyperedge HE-4 (induced by source paper "orthogonal graph drawing with inflexible edges"):
  with members:
    - "multiple source multiple sink maximum flow in directed planar graphs in near linear time"

Connected through hyperedge HE-5 (induced by source paper "complexity of higher degree orthogonal graph embedding in the kandinsky model"):
  with members:
    - "orthogonal graph drawing with inflexible edges"

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)