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: "a compact dnn approaching googlenet level accuracy of classification and domain adaptation"
  Abstract: Recently, DNN model compression based on network architecture design, e.g., SqueezeNet, attracted a lot attention. No accuracy drop on image classification is observed on these extremely compact networks, compared to well-known models. An emerging question, however, is whether these model compression techniques hurt DNN's learning ability other than classifying images on a single dataset. Our preliminary experiment shows that these compression methods could degrade domain adaptation (DA) ability, though the classification performance is preserved. Therefore, we propose a new compact network architecture and unsupervised DA method in this paper. The DNN is built on a new basic module Conv-M which provides more diverse feature extractors without significantly increasing parameters. The unified framework of our DA method will simultaneously learn invariance across domains, reduce divergence of feature representations, and adapt label prediction. Our DNN has 4.1M parameters, which is only 6.7% of AlexNet or 59% of GoogLeNet. Experiments show that our DNN obtains GoogLeNet-level accuracy both on classification and DA, and our DA method slightly outperforms previous competitive ones. Put all together, our DA strategy based on our DNN achieves state-of-the-art on sixteen of total eighteen DA tasks on popular Office-31 and Office-Caltech datasets.

Connected through hyperedge HE-1 (induced by source paper "cascaded coarse to fine deep kernel networks for efficient satellite image change detection"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep kernel learning"

Connected through hyperedge HE-2 (induced by source paper "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "zero shot deep domain adaptation"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "deep multimodal representation learning from temporal data"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "towards lightweight convolutional neural networks for object detection"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "shift a zero flop zero parameter alternative to spatial convolutions"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "aggregated residual transformations for deep neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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)