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: "maximizing sequence submodular functions and its application to online advertising"
  Abstract: Motivated by applications in online advertising, we consider a class of maximization problems where the objective is a function of the sequence of actions as well as the running duration of each action. For these problems, we introduce the concepts of \emph{sequence-submodularity} and \emph{sequence-monotonicity} which extend the notions of submodularity and monotonicity from functions defined over sets to functions defined over sequences. We establish that if the objective function is sequence-submodular and sequence-non-decreasing, then there exists a greedy algorithm that achieves $1-1/e$ of the optimal solution. We apply our algorithm and analysis to two applications in online advertising: online ad allocation and query rewriting. We first show that both problems can be formulated as maximizing non-decreasing sequence-submodular functions. We then apply our framework to these two problems, leading to simple greedy approaches with guaranteed performances. In particular, for online ad allocation problem the performance of our algorithm is $1-1/e$, which matches the best known existing performance, and for query rewriting problem the performance of our algorithm is $1- 1/e^{1-1/e}$ which improves upon the best known existing performance in the literature.

Connected through hyperedge HE-1 (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-2 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions"):
  with members:
    - "continuous dr submodular maximization structure and algorithms"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "maximizing monotone submodular functions over the integer lattice"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-3 (induced by source paper "placement and allocation of virtual network functions multi dimensional case"):
  with members:
    - "joint placement and allocation of virtual network functions with budget and capacity constraints"
    - "shortest path and maximum flow problems under service function chaining constraints"
    - "provably efficient algorithms for joint placement and allocation of virtual network functions"
    - "approximate and incremental network function placement"
    - "string submodular functions with curvature constraints"
    - "online admission control and embedding of service chains"
    - "competitive and deterministic embeddings of virtual networks"

Connected through hyperedge HE-4 (induced by source paper "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "welfare maximization with limited interaction"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "lazier than lazy greedy"

Connected through hyperedge HE-5 (induced by source paper "submodularity on hypergraphs from sets to sequences"):
  with members:
    - "string submodular functions with curvature constraints"
    - "submodular functions from discrete to continous domains"
    - "near optimally teaching the crowd to classify"
    - "distributed submodular maximization"
    - "near optimal nonmyopic value of information in graphical models"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "string submodular functions with curvature constraints"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "online combinatorial optimization for interconnected refrigeration systems linear approximation and submodularity"):
  with members:
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "approximation algorithms for bayesian multi armed bandit problems"):
  with members:
    - "active model selection"
    - "adaptive submodular optimization under matroid constraints"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "the ratio index for budgeted learning with applications"
    - "sequential design of experiments via linear programming"
    - "sponsored search auctions with markovian users"
    - "approximation algorithms for restless bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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.DM(Discrete Mathematics)