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: "graph bisection with pareto optimization"
  Abstract: We introduce FlowCutter, a novel algorithm to compute a set of edge cuts or node separators that optimize cut size and balance in the Pareto-sense. Our core algorithm solves the balanced connected st-edge-cut problem, where two given nodes s and t must be separated by removing edges to obtain two connected parts. Using the core algorithm we build variants that compute node separators and are independent of s and t. Using the Pareto-set we can identify cuts with a particularly good trade-off between cut size and balance that can be used to compute contraction and minimum fill-in orders, which can be used in Customizable Contraction Hierarchies (CCH), a speed-up technique for shortest path computations. Our core algorithm runs in O(cm) time where m is the number of edges and c the cut size. This makes it well-suited for large graphs with small cuts, such as road graphs, which are our primary application. For road graphs we present an extensive experimental study demonstrating that FlowCutter outperforms the current state of the art both in terms of cut sizes as well as CCH performance.

Connected through hyperedge HE-1 (induced by source paper "load balanced bottleneck objectives in process mapping"):
  with members:
    - "better process mapping and sparse quadratic assignment"
    - "recent advances in graph partitioning"

Connected through hyperedge HE-2 (induced by source paper "efficient contraction of large tensor networks for weighted model counting through graph decompositions"):
  with members:
    - "fast counting with tensor networks"
    - "benchmarking treewidth as a practical component of tensor network simulations"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "answer set solving with bounded treewidth revisited"
    - "positive instance driven dynamic programming for treewidth"
    - "faq questions asked frequently"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-3 (induced by source paper "space efficient fast and exact routing in time dependent road networks"):
  with members:
    - "faster and better nested dissection orders for customizable contraction hierarchies"
    - "improved oracles for time dependent road networks"
    - "engineering oracles for time dependent road networks"
    - "dynamic time dependent route planning in road networks with user preferences"
    - "route planning in transportation networks"
    - "customizable contraction hierarchies"

Connected through hyperedge HE-4 (induced by source paper "faster and better nested dissection orders for customizable contraction hierarchies"):
  with members:
    - "advanced multilevel node separator algorithms"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

Connected through hyperedge HE-5 (induced by source paper "evaluation of a flow based hypergraph bipartitioning algorithm"):
  with members:
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "memetic multilevel hypergraph partitioning"
    - "customizable contraction hierarchies"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-6 (induced by source paper "network flow based refinement for multilevel hypergraph partitioning"):
  with members:
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "memetic multilevel hypergraph partitioning"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "partitioning complex networks via size constrained clustering"
    - "engineering multilevel graph partitioning algorithms"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-7 (induced by source paper "distributed evolutionary k way node separators"):
  with members:
    - "advanced multilevel node separator algorithms"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

Connected through hyperedge HE-8 (induced by source paper "computing tree decompositions with flowcutter pace 2017 submission"):
  with members:
    - "route planning in transportation networks"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"

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)