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: "on the power of simple reductions for the maximum independent set problem"
  Abstract: Reductions—rules that reduce input size while maintaining the ability to compute an optimal solution—are critical for developing efficient maximum independent set algorithms in both theory and practice. While several simple reductions have previously been shown to make small domain-specific instances tractable in practice, it was only recently shown that advanced reductions (in a measure-and-conquer approach) can be used to solve real-world networks on millions of vertices [Akiba and Iwata, TCS 2016]. In this paper we compare these state-of-the-art reductions against a small suite of simple reductions, and come to two conclusions: just two simple reductions—vertex folding and isolated vertex removal—are sufficient for many real-world instances, and further, the power of the advanced rules comes largely from their initial application (i.e., kernelization), and not their repeated application during branch-and-bound. As a part of our comparison, we give the first experimental evaluation of a reduction based on maximum critical independent sets, and show it is highly effective in practice for medium-sized networks.

Connected through hyperedge HE-1 (induced by source paper "graph profiling for vertex cover targeted reductions in a branch and reduce solver"):
  with members:
    - "scalable kernelization for maximum independent sets"
    - "linear time fpt algorithms via network flow"
    - "faster parameterized algorithms using linear programming"

Connected through hyperedge HE-2 (induced by source paper "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"):
  with members:
    - "scalable kernelization for maximum independent sets"
    - "exact algorithms for maximum independent set"
    - "accelerating local search 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-3 (induced by source paper "exactly solving the maximum weight independent set problem on large real world graphs"):
  with members:
    - "scalable kernelization for maximum independent sets"

Connected through hyperedge HE-4 (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"
    - "finding near optimal independent sets at scale"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "critical and maximum independent sets of a graph"):
  with members:
    - "a set and collection lemma"
    - "on the core of a unicyclic graph"
    - "on the intersection of all critical sets of a unicyclic graph"
    - "vertices belonging to all critical independent sets of a graph"
    - "on the structure of the minimum critical independent set of a graph"
    - "critical sets in bipartite graphs"

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)