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: "distributed submodular maximization with limited information"
  Abstract: We consider a class of distributed submodular maximization problems in which each agent must choose a single strategy from its strategy set. The global objective is to maximize a submodular function of the strategies chosen by each agent. When choosing a strategy, each agent has access to only a limited number of other agents' choices. For each of its strategies, an agent can evaluate its marginal contribution to the global objective given its information. The main objective is to investigate how this limitation of information about the strategies chosen by other agents affects the performance when agents make choices according to a local greedy algorithm. In particular, we provide lower bounds on the performance of greedy algorithms for submodular maximization, which depend on the clique number of a graph that captures the information structure. We also characterize graph-theoretic upper bounds in terms of the chromatic number of the graph. Finally, we demonstrate how certain graph properties limit the performance of the greedy algorithm. Simulations on several common models for random networks demonstrate our results.

Connected through hyperedge HE-1 (induced by source paper "distributed submodular maximization with parallel execution"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "a parallel double greedy algorithm for submodular maximization"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-2 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking"):
  with members:
    - "distributed simultaneous action and target assignment for multi robot multi target tracking"
    - "active target tracking with self triggered communications in multi robot teams"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-3 (induced by source paper "distributed attack robust submodular maximization for multi robot planning"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "distributed state estimation using intermittently connected robot networks"
    - "robust monotone submodular function maximization"
    - "resilient active information gathering with mobile robots"
    - "resilient active target tracking with multiple robots"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "fast algorithms for the maximum clique problem on massive sparse graphs"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-4 (induced by source paper "a sub modular receding horizon solution for mobile multi agent persistent monitoring"):
  with members:
    - "multi robot routing for persistent monitoring with latency constraints"
    - "combinatorial algorithms for control of biological regulatory networks"
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"

Connected through hyperedge HE-5 (induced by source paper "distributed submodular minimization over networks a greedy column generation approach"):
  with members:
    - "reflection methods for user friendly submodular optimization"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "structured sparsity inducing norms through submodular functions"
    - "distributed abstract optimization via constraints consensus theory and applications"

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)