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: "online submodular maximization with preemption"
  Abstract: Submodular function maximization has been studied extensively in recent years under various constraints and models. The problem plays a major role in various disciplines. We study a natural online variant of this problem in which elements arrive one-by-one and the algorithm has to maintain a solution obeying certain constraints at all times. Upon arrival of an element, the algorithm has to decide whether to accept the element into its solution and may preempt previously chosen elements. The goal is to maximize a submodular function over the set of elements in the solution. #R##N#We study two special cases of this general problem and derive upper and lower bounds on the competitive ratio. Specifically, we design a $1/e$-competitive algorithm for the unconstrained case in which the algorithm may hold any subset of the elements, and constant competitive ratio algorithms for the case where the algorithm may hold at most $k$ elements in its solution.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"

Connected through hyperedge HE-2 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "the power of randomization distributed submodular maximization on massive datasets"

Connected through hyperedge HE-3 (induced by source paper "streaming submodular maximization under a k set system constraint"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "data summarization at scale a two stage submodular approach"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"

Connected through hyperedge HE-4 (induced by source paper "adaptive sequence submodularity"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-5 (induced by source paper "online non monotone dr submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-6 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular secretary problem with shortlists"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"

Connected through hyperedge HE-7 (induced by source paper "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"

Connected through hyperedge HE-8 (induced by source paper "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"):
  with members:
    - "deterministic 1 2 epsilon approximation for submodular maximization over a matroid"
    - "online submodular maximization beating 1 2 made simple"
    - "do less get more streaming submodular maximization with subsampling"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"

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)