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: "finding near optimal independent sets at scale"
  Abstract: The independent set problem is NP-hard and particularly difficult to solve in large sparse graphs. In this work, we develop an advanced evolutionary algorithm, which incorporates kernelization techniques to compute large independent sets in huge sparse networks. A recent exact algorithm has shown that large networks can be solved exactly by employing a branch-and-reduce technique that recursively kernelizes the graph and performs branching. However, one major drawback of their algorithm is that, for huge graphs, branching still can take exponential time. To avoid this problem, we recursively choose vertices that are likely to be in a large independent set (using an evolutionary approach), then further kernelize the graph. We show that identifying and removing vertices likely to be in large independent sets opens up the reduction space---which not only speeds up the computation of large independent sets drastically, but also enables us to compute high-quality independent sets on much larger instances than previously reported in the literature.

Connected through hyperedge HE-1 (induced by source paper "solving np hard problems on graphs with extended alphago zero"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks for maximum constraint satisfaction"):
  with members:
    - "graph colouring meets deep learning effective graph neural network models for combinatorial problems"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "learning a sat solver from single bit supervision"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "scalable kernelization for maximum independent sets"
    - "learning combinatorial optimization algorithms over graphs"
    - "a simple model to generate hard satisfiable instances"
    - "many hard examples in exact phase transitions with application to generating hard satisfiable instances"

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 "engineering kernelization for maximum cut"):
  with members:
    - "scalable kernelization for maximum independent sets"
    - "scalable generation of scale free graphs"
    - "improved kernels for signed max cut parameterized above lower bound on r l graphs"
    - "maximum balanced subgraph problem parameterized above lower bound"

Connected through hyperedge HE-5 (induced by source paper "exploring semi automatic map labeling"):
  with members:
    - "fully dynamic maximal independent set with sublinear update time"
    - "graph partitioning for independent sets"

Connected through hyperedge HE-6 (induced by source paper "combinatorial optimization with graph convolutional networks and guided tree search"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "learning combinatorial optimization algorithms over graphs"
    - "photographic image synthesis with cascaded refinement networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "independence number and the number of maximum independent sets in pseudofractal scale free web and sierpi nski gasket"):
  with members:
    - "domination number and minimum dominating sets in pseudofractal scale free web and sierpinski graph"
    - "accelerating local search for the maximum independent set problem"
    - "on approximating maximum independent set of rectangles"

Connected through hyperedge HE-8 (induced by source paper "scalable kernelization for maximum independent sets"):
  with members:
    - "exact algorithms for maximum independent set"
    - "accelerating local search for the maximum independent set problem"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "on the power of simple reductions for the maximum independent set problem"
    - "evaluation of labeling strategies for rotating maps"
    - "parallel graph partitioning for complex networks"
    - "kernelization using structural parameters on sparse graph classes"
    - "linear time fpt algorithms via network flow"

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)