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: "a parallel double greedy algorithm for submodular maximization"
  Abstract: We study parallel algorithms for the problem of maximizing a non-negative submodular function. Our main result is an algorithm that achieves a nearly-optimal $1/2 -\epsilon$ approximation using $O(\log(1/\epsilon) / \epsilon)$ parallel rounds of function evaluations. Our algorithm is based on a continuous variant of the double greedy algorithm of Buchbinder et al. that achieves the optimal $1/2$ approximation in the sequential setting. Our algorithm applies more generally to the problem of maximizing a continuous diminishing-returns (DR) function.

Connected through hyperedge HE-1 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "the fast algorithm for submodular maximization"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-2 (induced by source paper "distributed submodular maximization with parallel execution"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "distributed submodular maximization with limited information"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-3 (induced by source paper "non monotone submodular maximization with nearly optimal adaptivity and query complexity"):
  with members:
    - "fast parallel algorithms for feature selection"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"

Connected through hyperedge HE-4 (induced by source paper "fast parallel algorithms for feature selection"):
  with members:
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "the well tempered lasso"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with matroid and packing constraints in parallel"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-5 (induced by source paper "non monotone submodular maximization with nearly optimal adaptivity complexity"):
  with members:
    - "fast parallel algorithms for feature selection"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular maximization with matroid and packing constraints in parallel"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "unconstrained submodular maximization with constant adaptive complexity"

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)