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: "locally adaptive optimization adaptive seeding for monotone submodular functions"
  Abstract: The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global objective function. More generally, adaptive seeding is a stochastic optimization framework where the choices in the first stage affect the realizations in the second stage, over which we aim to optimize. #R##N#Our main result is a $(1-1/e)^2$-approximation for the adaptive seeding problem for any monotone submodular function. While adaptive policies are often approximated via non-adaptive policies, our algorithm is based on a novel method we call \emph{locally-adaptive} policies. These policies combine a non-adaptive global structure, with local adaptive optimizations. This method enables the $(1-1/e)^2$-approximation for general monotone submodular functions and circumvents some of the impossibilities associated with non-adaptive policies. #R##N#We also introduce a fundamental problem in submodular optimization that may be of independent interest: given a ground set of elements where every element appears with some small probability, find a set of expected size at most $k$ that has the highest expected value over the realization of the elements. We show a surprising result: there are classes of monotone submodular functions (including coverage) that can be approximated almost optimally as the probability vanishes. For general monotone submodular functions we show via a reduction from \textsc{Planted-Clique} that approximations for this problem are not likely to be obtainable. This optimization problem is an important tool for adaptive seeding via non-adaptive policies, and its hardness motivates the introduction of \emph{locally-adaptive} policies we use in the main result.

Connected through hyperedge HE-1 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-2 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "boosting information spread an algorithmic approach"

Connected through hyperedge HE-3 (induced by source paper "efficient approximation algorithms for adaptive target profit maximization"):
  with members:
    - "efficient approximation algorithms for adaptive seed minimization"
    - "coupon advertising in online social systems algorithms and sampling techniques"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "towards profit maximization for online social network providers"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "adaptive influence maximization with myopic feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "adaptive influence maximization under general feedback models"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a two phase investment game for competitive opinion dynamics in social networks"):
  with members:
    - "an integrated framework for competitive multi channel marketing of multi featured products"
    - "multi round influence maximization"
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "information diffusion in social networks in two phases"

Connected through hyperedge HE-6 (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"
    - "lazier than lazy greedy"
    - "the non adaptive query complexity of testing k parities"

Connected through hyperedge HE-7 (induced by source paper "optimal multiphase investment strategies for influencing opinions in a social network"):
  with members:
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "strategic disclosure of opinions on a social network"
    - "information diffusion in social networks in two phases"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "a multi phase approach for improving information diffusion in social networks"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-8 (induced by source paper "stochastic coupon probing in social networks"):
  with members:
    - "no time to observe adaptive influence maximization with partial feedback"
    - "optimizing ad allocation in social advertising"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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.SI(Social and Information Networks)