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 with correlated and noisy tests"
  Abstract: We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests. In contrast to prior work, we focus on the challenging, yet practically relevant setting where test outcomes can be conditionally dependent given the hidden target variable. Under such assumptions, common heuristics, such as greedily performing tests that maximize the reduction in uncertainty of the target, often perform poorly. In this paper, we propose ECED, a novel, computationally efficient active learning algorithm, and prove strong theoretical guarantees that hold with correlated, noisy tests. Rather than directly optimizing the prediction error, at each step, ECED picks the test that maximizes the gain in a surrogate objective, which takes into account the dependencies between tests. Our analysis relies on an information-theoretic auxiliary function to track the progress of ECED, and utilizes adaptive submodularity to attain the near-optimal bound. We demonstrate strong empirical performance of ECED on two problem instances, including a Bayesian experimental design task intended to distinguish among economic theories of how people make risky decisions, and an active preference learning task via pairwise comparisons.

Connected through hyperedge HE-1 (induced by source paper "learning in confusion batch active learning with noisy oracle"):
  with members:
    - "learning from noisy singly labeled data"
    - "learning deep networks from noisy labels with dropout regularization"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "active learning from imperfect labelers"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "noisy bayesian active learning"

Connected through hyperedge HE-2 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "diameter based active learning"
    - "clustering with same cluster queries"
    - "local algorithms for interactive clustering"
    - "efficient active learning of halfspaces an aggressive approach"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-3 (induced by source paper "bayesian active learning with abstention feedbacks"):
  with members:
    - "adaptive maximization of pointwise submodular functions with budget constraint"
    - "efficiency of active learning for the allocation of workers on crowdsourced classification tasks"
    - "robustness of bayesian pool based active learning against prior misspecification"
    - "active learning from weak and strong labelers"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "noisy bayesian active learning"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "data driven planning via imitation learning"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "deep reinforcement learning a brief survey"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "learning sampling distributions for robot motion planning"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "qmdp net deep learning for planning under partial observability"
    - "learning from the hindsight plan episodic mpc improvement"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "adaptive information gathering via imitation learning"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "pomdp lite for robust robot planning under uncertainty"
    - "plato policy learning using adaptive trajectory optimization"
    - "learning to gather information via imitation"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-6 (induced by source paper "learning to gather information via imitation"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "efficient touch based localization through submodularity"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"
    - "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)