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 noisy observations"
  Abstract: We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally. We show that if the observations are noisy, perhaps surprisingly, GBS can perform very poorly. We develop EC2, a novel, greedy active learning algorithm and prove that it is competitive with the optimal policy, thus obtaining the first competitiveness guarantees for Bayesian active learning with noisy observations. Our bounds rely on a recently discovered diminishing returns property called adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. Our results hold even if the tests have non-uniform cost and their noise is correlated. We also propose EffECXtive, a particularly fast approximation of EC2, and evaluate it on a Bayesian experimental design problem involving human subjects, intended to tease apart competing economic theories of how people make decisions under uncertainty.

Connected through hyperedge HE-1 (induced by source paper "active learning in the overparameterized and interpolating regime"):
  with members:
    - "two models of double descent for weak features"
    - "how do infinite width bounded norm networks look in function space"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-2 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a voting based system for ethical decision making"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "asking easy questions a user friendly approach to active reward learning"):
  with members:
    - "learning an urban air mobility encounter model from expert preferences"
    - "batch active learning using determinantal point processes"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"
    - "batch active preference based learning of reward functions"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "april active preference learning based reinforcement learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-6 (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"
    - "noisy bayesian active learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "improving user specifications for robot behavior through active preference learning framework and evaluation"):
  with members:
    - "bayesian active learning for collaborative task specification using equivalence regions"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "april active preference learning based reinforcement learning"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-8 (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"
    - "near optimal bayesian active learning for decision making"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "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)