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: "drawing large graphs by multilevel maxent stress optimization"
  Abstract: Drawing large graphs appropriately is an important step for the visual analysis of data from real-world networks. Here we present a novel multilevel algorithm to compute a graph layout with respect to a recently proposed metric that combines layout stress and entropy. As opposed to previous work, we do not solve the linear systems of the maxent-stress metric with a typical numerical solver. Instead we use a simple local iterative scheme within a multilevel approach. To accelerate local optimization, we approximate long-range forces and use shared-memory parallelism. Our experiments validate the high potential of our approach, which is particularly appealing for dynamic graphs. In comparison to the previously best maxent-stress optimizer, which is sequential, our parallel implementation is on average 30 times faster already for static graphs (and still faster if executed on one thread) while producing a comparable solution quality.

Connected through hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-2 (induced by source paper "batchlayout a batch parallel force directed graph layout algorithm in shared memory"):
  with members:
    - "multi level tree based approach for interactive graph visualization with semantic zoom"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "graph drawing by stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a distributed multilevel force directed algorithm"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-3 (induced by source paper "scalable graph algorithms"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "memetic graph clustering"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"

Connected through hyperedge HE-4 (induced by source paper "geometric crossing minimization a scalable randomized approach"):
  with members:
    - "a heuristic approach towards drawings of graphs with high crossing resolution"
    - "a greedy heuristic for crossing angle maximization"
    - "inserting multiple edges into a planar graph"

Connected through hyperedge HE-5 (induced by source paper "generating realistic scaled complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "generating scaled replicas of real world complex networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "tree based coarsening and partitioning of complex networks"
    - "recent advances in graph partitioning"

Connected through hyperedge HE-6 (induced by source paper "a distributed multilevel force directed algorithm"):
  with members:
    - "a distributed force directed algorithm on giraph design and experiments"
    - "a sparse stress model"
    - "parallel graph partitioning for complex networks"

Connected through hyperedge HE-7 (induced by source paper "a sparse stress model"):
  with members:
    - "coast a convex optimization approach to stress based embedding"

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)