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 multilevel bilinear programming algorithm for the vertex separator problem"
  Abstract: The Vertex Separator Problem for a graph is to find the smallest collection of vertices whose removal breaks the graph into two disconnected subsets that satisfy specified size constraints. In the paper 10.1016/j.ejor.2014.05.042, the Vertex Separator Problem was formulated as a continuous (non-concave/non-convex) bilinear quadratic program. In this paper, we develop a more general continuous bilinear program which incorporates vertex weights, and which applies to the coarse graphs that are generated in a multilevel compression of the original Vertex Separator Problem. A Mountain Climbing Algorithm is used to find a stationary point of the continuous bilinear quadratic program, while second-order optimality conditions and perturbation techniques are used to escape from either a stationary point or a local maximizer. The algorithms for solving the continuous bilinear program are employed during the solution and refinement phases in a multilevel scheme. Computational results and comparisons demonstrate the advantage of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "generating realistic scaled complex networks"
    - "generating scaled replicas of real world complex networks"
    - "networkit an interactive tool suite for high performance network analysis"
    - "recent advances in graph partitioning"
    - "community structure and scale free collections of erdős renyi graphs"
    - "advanced coarsening schemes for graph partitioning"
    - "multiscale network generation"

Connected through hyperedge HE-3 (induced by source paper "faster support vector machines"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "engineering multilevel support vector machines"
    - "parallel graph partitioning for complex networks"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "parallel graph decompositions using random shifts"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-4 (induced by source paper "distributed evolutionary k way node separators"):
  with members:
    - "advanced multilevel node separator algorithms"
    - "graph bisection with pareto optimization"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

Connected through hyperedge HE-5 (induced by source paper "advanced multilevel node separator algorithms"):
  with members:
    - "graph bisection with pareto optimization"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"
    - "n level graph partitioning"
    - "engineering multilevel graph partitioning algorithms"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-6 (induced by source paper "recent advances in graph partitioning"):
  with members:
    - "on the parameterized complexity of computing balanced partitions in graphs"
    - "semi external algorithms for graph partitioning and clustering"
    - "tree based coarsening and partitioning of complex networks"
    - "parallel graph partitioning for complex networks"
    - "algorithms for mapping parallel processes onto grid and torus architectures"
    - "partitioning complex networks via size constrained clustering"
    - "community detection and graph partitioning"
    - "efficient parallel and external matching"

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)