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 "para active learning"):
  Abstract: Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to build a generic strategy for parallelizing learning algorithms. This strategy is effective because the search for informative examples is highly parallelizable and because we show that its performance does not deteriorate when the sifting process relies on a slightly outdated model. Parallel active learning is particularly attractive to train nonlinear models with non-linear representations because there are few practical parallel learning algorithms for such models. We report preliminary experiments using both kernel SVMs and SGD-trained neural networks.

Includes members:
  Member "efficient protocols for distributed classification and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed and streaming linear programming in low dimensions")
  Member "distributed learning communication complexity and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on distributed machine learning")
  Member "a reliable effective terascale linear learning system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")
  Member "online importance weight aware updates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "warm starting contextual bandits robustly combining supervised and bandit feedback")
  Member "agnostic active learning without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "adaptive bound optimization for online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "optimal distributed online prediction using mini batches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "importance weighted active learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive region based active 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)