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: "resfeats residual network based features for image classification"
  Abstract: Deep residual networks have recently emerged as the state-of-the-art architecture in image segmentation and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we propose to use PCA for dimensionality reduction. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features.

Connected through hyperedge HE-1 (induced by source paper "deep learning based feature extraction in iris recognition use existing models fine tune or train from scratch"):
  with members:
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "an experimental study of deep convolutional features for iris recognition"
    - "the nd iris 0405 iris image dataset"
    - "deep residual learning for image recognition"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "automatic hierarchical classification of kelps using deep residual features"):
  with members:
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "video to events bringing modern computer vision closer to event cameras"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-4 (induced by source paper "analysis dictionary learning based classification structure for robustness"):
  with members:
    - "deep dictionary learning a parametric network approach"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "deep residual learning for image recognition"
    - "visualizing and understanding convolutional networks"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-5 (induced by source paper "recurrent knowledge distillation"):
  with members:
    - "sharesnet reducing residual network parameter number by sharing weights"
    - "mask r cnn"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "sketching and neural networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "feature pyramid networks for object detection"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "on the behavior of convolutional nets for feature extraction"):
  with members:
    - "full network embedding in a multimodal embedding pipeline"
    - "an out of the box full network embedding for convolutional neural networks"
    - "places an image database for deep scene understanding"
    - "understanding neural networks through deep visualization"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)