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: "string submodular functions with curvature constraints"
  Abstract: Consider the problem of choosing a string of actions to optimize an objective function that is string submodular. It was shown in previous papers that the greedy strategy, consisting of a string of actions that only locally maximizes the step-wise gain in the objective function, achieves at least a    $(1-e^{-1})$  -approximation to the optimal strategy. This paper improves this approximation by introducing additional constraints on curvature, namely, total backward curvature, total forward curvature, and elemental forward curvature. We show that if the objective function has total backward curvature    $\sigma$  , then the greedy strategy achieves at least a    $(1/\sigma)(1-e^{-\sigma})$  -approximation of the optimal strategy. If the objective function has total forward curvature    $\epsilon$  , then the greedy strategy achieves at least a    $(1-\epsilon)$  -approximation of the optimal strategy. Moreover, we consider a generalization of the diminishing-return property by defining the elemental forward curvature. We also introduce the notion of string-matroid and consider the problem of maximizing the objective function subject to a string-matroid constraint. We investigate two applications of string submodular functions with curvature constraints: 1) choosing a string of actions to maximize the expected fraction of accomplished tasks; and 2) designing a string of measurement matrices such that the information gain is maximized.

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 "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"
    - "online admission control and embedding of service chains"
    - "competitive and deterministic embeddings of virtual networks"
    - "maximizing sequence submodular functions and its application to online advertising"

Connected through hyperedge HE-3 (induced by source paper "teaching multiple concepts to forgetful learners"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "the inaturalist species classification and detection dataset"
    - "unbounded human learning optimal scheduling for spaced repetition"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "submodularity on hypergraphs from sets to sequences"):
  with members:
    - "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"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deterministic adaptive non submodular maximization via the primal curvature"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-6 (induced by source paper "subspace selection for projection maximization with matroid constraints"):
  with members:
    - "neighbor discovery for wireless networks via compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "the geometry of fusion inspired channel design"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "greedy adaptive compression in signal plus noise models"
    - "hessian and concavity of mutual information differential entropy and entropy power in linear vector gaussian channels"
    - "mutual information and minimum mean square error in gaussian channels"

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)