human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bayesian active learning with abstention feedbacks"):
  Abstract: We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. Using the Bayesian approach, we develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.

Includes members:
  Member "near optimal bayesian active learning with correlated and noisy tests":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "adaptive maximization of pointwise submodular functions with budget constraint":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient representative subset selection over sliding windows")
  Member "efficiency of active learning for the allocation of workers on crowdsourced classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial task assignment")
  Member "robustness of bayesian pool based active learning against prior misspecification":
    also connected through other hyperedges:
      (none)
  Member "active learning from weak and strong labelers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a graph based approach for active learning in regression")
  Member "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "noisy bayesian active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "near optimal bayesian active learning with noisy observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "active learning in the overparameterized and interpolating regime")

Question: Please tell me which class the source paper 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)