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: "a heuristic approach towards drawings of graphs with high crossing resolution"
  Abstract: The crossing resolution of a non-planar drawing of a graph is the value of the minimum angle formed by any pair of crossing edges. Recent experiments have shown that the larger the crossing resolution is, the easier it is to read and interpret a drawing of a graph. However, maximizing the crossing resolution turns out to be an NP-hard problem in general and only heuristic algorithms are known that are mainly based on appropriately adjusting force-directed algorithms. #R##N#In this paper, we propose a new heuristic algorithm for the crossing resolution maximization problem and we experimentally compare it against the known approaches from the literature. Our experimental evaluation indicates that the new heuristic produces drawings with better crossing resolution, but this comes at the cost of slightly higher aspect ratio, especially when the input graph is large.

Connected through hyperedge HE-1 (induced by source paper "stress plus x spx graph layout"):
  with members:
    - "a greedy heuristic for crossing angle maximization"
    - "an overview of gradient descent optimization algorithms"
    - "maximizing the total resolution of graphs"

Connected through hyperedge HE-2 (induced by source paper "geometric crossing minimization a scalable randomized approach"):
  with members:
    - "a greedy heuristic for crossing angle maximization"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "inserting multiple edges into a planar graph"

Connected through hyperedge HE-3 (induced by source paper "graphs with large total angular resolution"):
  with members:
    - "maximizing the total resolution of graphs"
    - "the straight line rac drawing problem is np hard"
    - "notes on large angle crossing graphs"

Connected through hyperedge HE-4 (induced by source paper "a survey on graph drawing beyond planarity"):
  with members:
    - "parameterized complexity of 1 planarity"
    - "minimum weight connectivity augmentation for planar straight line graphs"
    - "compact drawings of 1 planar graphs with right angle crossings and few bends"
    - "drawing subcubic 1 planar graphs with few bends few slopes and large angles"
    - "turning cliques into paths to achieve planarity"
    - "on rac drawings of graphs with one bend per edge"
    - "recognizing optimal 1 planar graphs in linear time"
    - "orthogonal and smooth orthogonal layouts of 1 planar graphs with low edge complexity"

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)