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 baseline for visual instance retrieval with deep convolutional networks"
  Abstract: This paper presents a simple pipeline for visual instance retrieval exploiting image representations based on convolutional networks (ConvNets), and demonstrates that ConvNet image representations outperform other state-of-the-art image representations on six standard image retrieval datasets for the first time. Unlike existing design choices, our image representation does not require fine-tuning or learning with data similar to the test set. Furthermore, we consider the challenge "Can you construct a tiny image representation with memory requirements less than or equal to 32 bytes that can successfully perform retrieval?" We report the promising performance of our tiny ConvNet based representation.

Connected through hyperedge HE-1 (induced by source paper "deep exemplar based colorization"):
  with members:
    - "stereoscopic neural style transfer"
    - "real time user guided image colorization with learned deep priors"
    - "photographic image synthesis with cascaded refinement networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "stochastic attraction repulsion embedding for large scale image localization"):
  with members:
    - "cplanet enhancing image geolocalization by combinatorial partitioning of maps"
    - "sampling matters in deep embedding learning"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "deep metric learning with angular loss"
    - "revisiting im2gps in the deep learning era"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-3 (induced by source paper "geometric loss functions for camera pose regression with deep learning"):
  with members:
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "relative camera pose estimation using convolutional neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "planet photo geolocation with convolutional neural networks"
    - "image based localization using lstms for structured feature correlation"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "compression of deep neural networks for image instance retrieval"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "group invariant deep representations for image instance retrieval"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-5 (induced by source paper "deep image retrieval learning global representations for image search"):
  with members:
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "multiple measurements and joint dimensionality reduction for large scale image search with short vectors extended version"
    - "vqa visual question answering"
    - "spatial transformer networks"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "aggregating deep convolutional features for image retrieval"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "good practice in cnn feature transfer"):
  with members:
    - "deep convolutional features for image based retrieval and scene categorization"
    - "exploiting local features from deep networks for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "generalized max pooling"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"

Connected through hyperedge HE-8 (induced by source paper "faster r cnn features for instance search"):
  with members:
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "fast r cnn"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "neural codes for image retrieval"

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)