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: "the impact of information in distributed submodular maximization"
  Abstract: The maximization of submodular functions is an NP-Hard problem for certain subclasses of functions, for which a simple greedy algorithm has been shown to guarantee a solution whose quality is within 1/2 of the optimal. When this algorithm is implemented in a distributed way, agents sequentially make decisions based on the decisions of all previous agents. This work explores how limited access to the decisions of previous agents affects the quality of the solution of the greedy algorithm. Specifically, we provide tight upper and lower bounds on how well the algorithm performs, as a function of the information available to each agent. Intuitively, the results show that performance roughly degrades proportionally to the size of the largest group of agents that make decisions independently. Additionally, we consider the case where a system designer is given a set of agents and a global limit on the amount of information that can be accessed. Our results show that the best designs partition the agents into equally sized sets and allow agents to access the decisions of all previous agents within the same set.

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

Connected through hyperedge HE-2 (induced by source paper "distributed attack robust submodular maximization for multi robot planning"):
  with members:
    - "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"
    - "distributed submodular maximization with limited information"
    - "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-3 (induced by source paper "threshold greedy based task allocation for multiple robot operations"):
  with members:
    - "greed is good near optimal submodular maximization via greedy optimization"

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)