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: "iamnn iterative and adaptive mobile neural network for efficient image classification"
  Abstract: Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing gradients. These shortcut connections have interesting side-effects that make ResNets behave differently from other typical network architectures. In this work we use these properties to design a network based on a ResNet but with parameter sharing and with adaptive computation time. The resulting network is much smaller than the original network and can adapt the computational cost to the complexity of the input image.

Connected through hyperedge HE-1 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "thundernet towards real time generic object detection"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "adaptive and iteratively improving recurrent lateral connections"):
  with members:
    - "video action recognition via neural architecture searching"
    - "graph hypernetworks for neural architecture search"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "densepose dense human pose estimation in the wild"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "brain like object recognition with high performing shallow recurrent anns"):
  with members:
    - "do better imagenet models transfer better"
    - "exploring the limits of weakly supervised pretraining"
    - "progressive neural architecture search"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual connections encourage iterative inference"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "select attend and transfer light learnable skip connections"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual attention network for image classification"
    - "channel pruning for accelerating very deep neural networks"
    - "skip connections as effective symmetry breaking"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "layer flexible adaptive computational time for recurrent neural networks"):
  with members:
    - "universal transformers"
    - "depth adaptive computational policies for efficient visual tracking"
    - "dynamic prediction length for time series with sequence to sequence networks"
    - "early improving recurrent elastic highway network"
    - "learning to skim text"
    - "dynamic computational time for visual attention"
    - "learning to reason with adaptive computation"
    - "convolutional neural fabrics"

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)