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 "adaptive region based active learning"):
  Abstract: We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active learning baselines.

Includes members:
  Member "efficient active learning of sparse halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "on the relationship between data efficiency and error for uncertainty sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selection via proxy efficient data selection for deep learning")
  Member "diameter based active learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diameter based interactive structure discovery")
  Member "efficient learning of linear separators under bounded noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "efficient and parsimonious agnostic active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep active learning with a neural architecture search")
  Member "beyond disagreement based agnostic active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bias aware heapified policy for active learning")
  Member "minimax analysis of active learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise tolerant reliable active classification with comparison queries")
  Member "active and passive learning of linear separators under log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise tolerant reliable active classification with comparison queries")

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)