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: "adaptive submodular optimization under matroid constraints"
  Abstract: Many important problems in discrete optimization require maximization of a monotonic submodular function subject to matroid constraints. For these problems, a simple greedy algorithm is guaranteed to obtain near-optimal solutions. In this article, we extend this classic result to a general class of adaptive optimization problems under partial observability, where each choice can depend on observations resulting from past choices. Specifically, we prove that a natural adaptive greedy algorithm provides a $1/(p+1)$ approximation for the problem of maximizing an adaptive monotone submodular function subject to $p$ matroid constraints, and more generally over arbitrary $p$-independence systems. We illustrate the usefulness of our result on a complex adaptive match-making application.

Connected through hyperedge HE-1 (induced by source paper "on maximizing a monotone k submodular function subject to a matroid constraint"):
  with members:
    - "maximizing k submodular functions and beyond"
    - "improved approximation algorithms for k submodular function maximization"
    - "towards minimizing k submodular functions"
    - "potts model parametric maxflow and k submodular functions"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"

Connected through hyperedge HE-2 (induced by source paper "adaptive influence maximization in social networks why commit when you can adapt"):
  with members:
    - "interactive submodular set cover"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "submodular stochastic probing on matroids"):
  with members:
    - "bounds on the lambert function and their application to the outage analysis of user cooperation"
    - "a stochastic probing problem with applications"
    - "matroid prophet inequalities"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "mechanism design via correlation gap"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "approximation algorithms for bayesian multi armed bandit problems"):
  with members:
    - "active model selection"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adaptive submodularity theory and applications in active learning and stochastic optimization"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal nonmyopic value of information in graphical models"
    - "near optimal bayesian active learning with noisy observations"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "interactive submodular set cover"
    - "average case active learning with costs"
    - "optimal value of information in graphical models"
    - "online planning algorithms for pomdps"

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)