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: "exactly solving the maximum weight independent set problem on large real world graphs"
  Abstract: One powerful technique to solve NP-hard optimization problems in practice is branch-and-reduce search---which is branch-and-bound that intermixes branching with reductions to decrease the input size. While this technique is known to be very effective in practice for unweighted problems, very little is known for weighted problems, in part due to a lack of known effective reductions. In this work, we develop a full suite of new reductions for the maximum weight independent set problem and provide extensive experiments to show their effectiveness in practice on real-world graphs of up to millions of vertices and edges. #R##N#Our experiments indicate that our approach is able to outperform existing state-of-the-art algorithms, solving many instances that were previously infeasible. In particular, we show that branch-and-reduce is able to solve a large number of instances up to two orders of magnitude faster than existing (inexact) local search algorithms---and is able to solve the majority of instances within 15 minutes. For those instances remaining infeasible, we show that combining kernelization with local search produces higher-quality solutions than local search alone.

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"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"
    - "shared memory exact minimum cuts"

Connected through hyperedge HE-2 (induced by source paper "scalable graph algorithms"):
  with members:
    - "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"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-3 (induced by source paper "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"):
  with members:
    - "experimental evaluation of kernelization algorithms to dominating set"
    - "polynomial bounds for centered colorings on proper minor closed graph classes"
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "distributed domination on graph classes of bounded expansion"
    - "reconfiguration on nowhere dense graph classes"
    - "positive instance driven dynamic programming for treewidth"
    - "experimental evaluation of counting subgraph isomorphisms in classes of bounded expansion"
    - "on the number of types in sparse 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)