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: "comparing apples and oranges query tradeoff in submodular maximization"
  Abstract: Fast algorithms for submodular maximization problems have a vast potential use in applicative settings, such as machine learning, social networks, and economics. Though fast algorithms were known for some special cases, only recently Badanidiyuru and Vondr\'{a}k (2014) were the first to explicitly look for such algorithms in the general case of maximizing a monotone submodular function subject to a matroid independence constraint. The algorithm of Badanidiyuru and Vondr\'{a}k matches the best possible approximation guarantee, while trying to reduce the number of value oracle queries the algorithm performs. #R##N#Our main result is a new algorithm for this general case which establishes a surprising tradeoff between two seemingly unrelated quantities: the number of value oracle queries and the number of matroid independence queries performed by the algorithm. Specifically, one can decrease the former by increasing the latter and vice versa, while maintaining the best possible approximation guarantee. Such a tradeoff is very useful since various applications might incur significantly different costs in querying the value and matroid independence oracles. Furthermore, in case the rank of the matroid is $O(n^c)$, where $n$ is the size of the ground set and $c$ is an absolute constant smaller than $1$, the total number of oracle queries our algorithm uses can be made to have a smaller magnitude compared to that needed by Badanidiyuru and Vondr\'{a}k. We also provide even faster algorithms for the well studied special cases of a cardinality constraint and a partition matroid independence constraint, both of which capture many real-world applications and have been widely studied both theorically and in practice.

Connected through hyperedge HE-1 (induced by source paper "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"):
  with members:
    - "parallel algorithm for non monotone dr submodular maximization"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"

Connected through hyperedge HE-2 (induced by source paper "fast non monotone submodular maximisation subject to a matroid constraint"):
  with members:
    - "deep submodular functions"
    - "constrained submodular maximization via a non symmetric technique"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "a note on concentration of submodular functions"
    - "submodular maximization by simulated annealing"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-3 (induced by source paper "greed is good near optimal submodular maximization via greedy optimization"):
  with members:
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "deterministic algorithms for submodular maximization problems"
    - "lazier than lazy greedy"
    - "near optimally teaching the crowd to classify"
    - "a k 3 2 approximation algorithm for monotone submodular k set packing and general k exchange systems"
    - "symmetry and approximability of submodular maximization problems"

Connected through hyperedge HE-4 (induced by source paper "a new framework for distributed submodular maximization"):
  with members:
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "lazier than lazy greedy"
    - "parallel algorithms for geometric graph problems"
    - "communication steps for parallel query processing"
    - "sorting searching and simulation in the mapreduce framework"
    - "symmetry and approximability of submodular maximization problems"
    - "constrained non monotone submodular maximization offline and secretary algorithms"

Connected through hyperedge HE-5 (induced by source paper "deterministic algorithms for submodular maximization problems"):
  with members:
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"
    - "a deterministic algorithm for maximizing submodular functions"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular maximization by simulated annealing"

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)