human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "falcon feature driven selective classification for energy efficient image recognition"
  Abstract: Machine-learning algorithms have shown outstanding image recognition or classification performance for computer vision applications. However, the compute and energy requirement for implementing such classifier models for large-scale problems is quite high. In this paper, we propose Feature Driven Selective Classification (FALCON) inspired by the biological visual attention mechanism in the brain to optimize the energy-efficiency of machine-learning classifiers. We use the consensus in the characteristic features (color/texture) across images in a dataset to decompose the original classification problem and construct a tree of classifiers (nodes) with a generic-to-specific transition in the classification hierarchy. The initial nodes of the tree separate the instances based on feature information and selectively enable the latter nodes to perform object specific classification. The proposed methodology allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. Additionally, we propose a programmable and scalable Neuromorphic Engine (NeuE) that utilizes arrays of specialized neural computational elements to execute the FALCON based classifier models for diverse datasets. The structure of FALCON facilitates the reuse of nodes while scaling up from small classification problems to larger ones thus allowing us to construct classifier implementations that are significantly more efficient. We evaluate our approach for a 12-object classification task on the Caltech101 dataset and 10-object task on CIFAR-10 dataset by constructing FALCON models on the NeuE platform in 45nm technology. Our results demonstrate significant improvement in energy-efficiency and training time for minimal loss in output quality.

Connected through hyperedge HE-1 (induced by source paper "energy efficient and robust cumulative training with net2net transformation"):
  with members:
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "incremental learning in deep convolutional neural networks using partial network sharing"
    - "learning without forgetting"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "designing adaptive neural networks for energy constrained image classification"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adaptive neural networks for efficient inference"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "hyperpower power and memory constrained hyper parameter optimization for neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"

Connected through hyperedge HE-3 (induced by source paper "tree cnn a hierarchical deep convolutional neural network for incremental learning"):
  with members:
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "deep convolutional neural networks as generic feature extractors"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "learning without forgetting"
    - "expert gate lifelong learning with a network of experts"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "incremental learning in deep convolutional neural networks using partial network sharing"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "progressive neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-5 (induced by source paper "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"):
  with members:
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-6 (induced by source paper "energy efficient object detection using semantic decomposition"):
  with members:
    - "what makes for effective detection proposals"

Question: Please tell me which class the center node 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.CV(Computer Vision and Pattern Recognition)