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: "near optimal bayesian active learning for decision making"
  Abstract: How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses. Our goal is to drive uncertainty into a single decision region as quickly as possible. #R##N#We identify necessary and sufficient conditions for correctly identifying a decision region that contains all hypotheses consistent with observations. We develop a novel Hyperedge Cutting (HEC) algorithm for this problem, and prove that is competitive with the intractable optimal policy. Our efficient implementation of the algorithm relies on computing subsets of the complete homogeneous symmetric polynomials. Finally, we demonstrate its effectiveness on two practical applications: approximate comparison-based learning and active localization using a robot manipulator.

Connected through hyperedge HE-1 (induced by source paper "adaptivity in adaptive submodularity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "stochastic submodular cover with limited adaptivity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"

Connected through hyperedge HE-2 (induced by source paper "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"):
  with members:
    - "adaptive feature selection computationally efficient online sparse linear regression under rip"
    - "optimal pricing for submodular valuations with bounded curvature"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "active learning for visual question answering an empirical study"):
  with members:
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "the promise of premise harnessing question premises in visual question answering"
    - "deep bayesian active learning with image data"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "leveraging visual question answering for image caption ranking"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-4 (induced by source paper "adaptive matching for expert systems with uncertain task types"):
  with members:
    - "matching while learning"
    - "enhancing the delay performance of dynamic backpressure algorithms"
    - "approval voting and incentives in crowdsourcing"
    - "delay based back pressure scheduling in multihop wireless networks"
    - "backpressure with adaptive redundancy bwar"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"):
  with members:
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "optimal value of information in graphical models"

Connected through hyperedge HE-6 (induced by source paper "adaptive submodular ranking"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "scenario submodular cover"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "analytical approach to parallel repetition"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "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-7 (induced by source paper "scenario submodular cover"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "adaptive submodular ranking"
    - "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "adaptive maximization of pointwise submodular functions with budget constraint"):
  with members:
    - "interactive submodular set cover"
    - "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.LG(Machine Learning)