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: "scalable kernelization for maximum independent sets"
  Abstract: The most efficient algorithms for finding maximum independent sets in both theory and practice use reduction rules to obtain a much smaller problem instance called a kernel. The kernel can then be solved quickly using exact or heuristic algorithms - or by repeatedly kernelizing recursively in the branch-and-reduce paradigm. It is of critical importance for these algorithms that kernelization is fast and returns a small kernel. Current algorithms are either slow but produce a small kernel, or fast and give a large kernel. We attempt to accomplish both of these goals simultaneously, by giving an efficient parallel kernelization algorithm based on graph partitioning and parallel bipartite maximum matching. We combine our parallelization techniques with two techniques to accelerate kernelization further: dependency checking that prunes reductions that cannot be applied, and reduction tracking that allows us to stop kernelization when reductions become less fruitful. Our algorithm produces kernels that are orders of magnitude smaller than the fastest kernelization methods, while having a similar execution time. Furthermore, our algorithm is able to compute kernels with size comparable to the smallest known kernels, but up to two orders of magnitude faster than previously possible. Finally, we show that our kernelization algorithm can be used to accelerate existing state-of-the-art heuristic algorithms, allowing us to find larger independent sets faster on large real-world networks and synthetic instances.

Connected through hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice"):
  with members:
    - "engineering kernelization for maximum cut"
    - "shared memory branch and reduce for multiterminal cuts"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"

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"
    - "learning combinatorial optimization algorithms over graphs"
    - "finding near optimal independent sets at scale"
    - "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 "graph profiling for vertex cover targeted reductions in a branch and reduce solver"):
  with members:
    - "on the power of simple reductions for the maximum independent set problem"
    - "linear time fpt algorithms via network flow"
    - "faster parameterized algorithms using linear programming"

Connected through hyperedge HE-4 (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"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-5 (induced by source paper "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"):
  with members:
    - "exact algorithms for maximum independent set"
    - "accelerating local search for the maximum independent set problem"
    - "on the power of simple reductions for the maximum independent set problem"
    - "trajectory based dynamic map labeling"
    - "efficient classification for metric data"
    - "linear time fpt algorithms via network flow"

Connected through hyperedge HE-6 (induced by source paper "engineering kernelization for maximum cut"):
  with members:
    - "scalable generation of scale free graphs"
    - "finding near optimal independent sets at scale"
    - "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-7 (induced by source paper "exactly solving the maximum weight independent set problem on large real world graphs"):
  with members:
    - "on the power of simple reductions for the maximum independent set problem"

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)