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 "deep active learning unified and principled method for query and training"):
  Abstract: In this paper, we proposed a unified and principled method for both querying and training process in deep batch active learning. We provided the theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting Wasserstein distance. As a consequence, we derived a new training loss from the theoretical analysis, which is decomposed into optimizing deep neural network parameter and batch query selection through alternative optimization. In addition, the loss for training deep neural network is naturally formulated as a min-max optimization problem through leveraging the unlabeled data information. Moreover, the proposed principles also indicate an explicit uncertainty-diversity trade-off in the query batch selection. Finally we evaluated our proposed method for different benchmarks, showing consistently better empirical performances and more time efficient query strategy, comparing to several baselines.

Includes members:
  Member "bayesian generative active deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning")
  Member "deep batch active learning by diverse uncertain gradient lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information condensing active learning")
  Member "variational adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task aware variational adversarial active learning")
  Member "a principled approach for learning task similarity in multitask learning":
    also connected through other hyperedges:
      (none)
  Member "adversarial active learning for deep networks a margin based approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information condensing active learning")
  Member "adversarial sampling for active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active adversarial domain adaptation")
  Member "generative adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "baod budget aware object detection")
  Member "learning algorithms for active learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")

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)