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 "designing adaptive neural networks for energy constrained image classification"):
  Abstract: As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., systems of networks with different accuracy and computation characteristics, where a selection scheme adaptively selects the network to be evaluated for each input image. While previous efforts have investigated different network selection schemes, we find that they do not necessarily result in energy savings when deployed on mobile systems. The key limitation of existing methods is that they learn only how data should be processed among the CNNs and not the network architectures, with each network being treated as a blackbox. #R##N#To address this limitation, we pursue a more powerful design paradigm where the architecture settings of the CNNs are treated as hyper-parameters to be globally optimized. We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device. To efficiently solve this problem, we adapt Bayesian optimization to the properties of the design space, reaching near-optimal configurations in few tens of function evaluations. Our method reduces the energy consumed for image classification on a mobile device by up to 6x, compared to the best previously published work that uses CNNs as blackboxes. Finally, we evaluate two image classification practices, i.e., classifying all images locally versus over the cloud under energy and communication constraints.

Includes members:
  Member "tree cnn a deep convolutional neural network for lifelong learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structured compression and sharing of representational space for continual learning")
  Member "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors")
  Member "adaptive neural networks for efficient inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "neuralpower predict and deploy energy efficient convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")
  Member "morphnet fast simple resource constrained structure learning of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural architecture search over decentralized data")
  Member "distributed deep neural networks over the cloud the edge and end devices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "run time deep model multiplexing")
  Member "hyperpower power and memory constrained hyper parameter optimization for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "single path mobile automl efficient convnet design and nas hyperparameter optimization")
  Member "nest a neural network synthesis tool based on a grow and prune paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")

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)