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: "noisy bayesian active learning"
  Abstract: Consider the problem of noisy Bayesian active learning given a sample space, a finite label set, and a finite set of label generating functions from the sample space to the label set, also known as the function class. The objective is to identify the function in the function class that generates the labels using as few label queries as possible and with low probability of error despite possible corruption by independent noise. The key to achieving this objective relies on the selection of queries in a strategic and adaptive manner. The problem generalizes the problem of noisy generalized binary search [1]. This paper explores the connection between the above Bayesian active learning problem and the problem of information acquisition in which a Bayesian decision maker is responsible for dynamically collecting noisy observations so as to enhance her/his information in a speedy manner about an underlying phenomena of interest with a constraint on the probability of error. This view of the problem allows for developing a general lower bound on the expected number of queries to identify the function that generates the labels in terms of the observation noise statistics, the desired probability of error, and the cardinality of the function class. Furthermore, viewing the problem as an information acquisition problem enables a deterministic and Markov heuristic policy based on greedy maximization of Extrinsic Jensen-Shannon divergence [2]. The performance of this heuristic is compared with the state of the art strategies for noisy generalized binary search. In the case where the function class is sample-rich, it is shown that this heuristic is better than previous results and, in particular, matches the earlier proposed lower bound asymptotically.

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"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "active learning from imperfect labelers"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "bayesian active learning with abstention feedbacks"):
  with members:
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "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"
    - "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 "a tight analysis of greedy yields subexponential time approximation for uniform decision tree"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "inapproximability of vc dimension and littlestone s dimension"
    - "inapproximability of nash equilibrium"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

Connected through hyperedge HE-4 (induced by source paper "super resolution of time series labels for bootstrapped event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-5 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-6 (induced by source paper "active classification for pomdps a kalman like state estimator"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "near optimal adaptive compressed sensing"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "near optimal bayesian active learning with noisy observations"
    - "algorithms for dynamic spectrum access with learning for cognitive radio"

Connected through hyperedge HE-7 (induced by source paper "beyond disagreement based agnostic active learning"):
  with members:
    - "the power of localization for efficiently learning linear separators with noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"
    - "a tutorial on conformal prediction"

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.IT(Information Theory)