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: "convolutional patch representations for image retrieval an unsupervised approach"
  Abstract: Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision. While excellent performance was achieved for image classification when large amounts of labeled visual data are available, their success for un-supervised tasks such as image retrieval has been moderate so far. Our paper focuses on this latter setting and explores several methods for learning patch descriptors without supervision with application to matching and instance-level retrieval. To that effect, we propose a new family of convolutional descriptors for patch representation , based on the recently introduced convolutional kernel networks. We show that our descriptor, named Patch-CKN, performs better than SIFT as well as other convolutional networks learned by artificially introducing supervision and is significantly faster to train. To demonstrate its effectiveness, we perform an extensive evaluation on standard benchmarks for patch and image retrieval where we obtain state-of-the-art results. We also introduce a new dataset called RomePatches, which allows to simultaneously study descriptor performance for patch and image retrieval.

Connected through hyperedge HE-1 (induced by source paper "automatic classification of defective photovoltaic module cells in electroluminescence images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "exploiting local features from deep networks for image retrieval"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "semi supervised feature learning for improving writer identification"):
  with members:
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "a siamese long short term memory architecture for human re identification"
    - "deep residual learning for image recognition"
    - "semi supervised learning with ladder networks"
    - "rethinking the inception architecture for computer vision"
    - "sparse radial sampling lbp for writer identification"

Connected through hyperedge HE-3 (induced by source paper "texture synthesis guided deep hashing for texture image retrieval"):
  with members:
    - "local neighborhood intensity pattern a new texture feature descriptor for image retrieval"
    - "non stationary texture synthesis by adversarial expansion"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "hashing with binary matrix pursuit"
    - "deep supervised discrete hashing"
    - "texturegan controlling deep image synthesis with texture patches"
    - "fine tuning cnn image retrieval with no human annotation"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-4 (induced by source paper "understanding and improving kernel local descriptors"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "multiple kernel local patch descriptor"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "visualizing deep convolutional neural networks using natural pre images"

Connected through hyperedge HE-5 (induced by source paper "semi supervised feature learning for off line writer identifications"):
  with members:
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "semi supervised learning with ladder networks"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "copy move forgery detection based on convolutional kernel network"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "predicting urban water quality with ubiquitous data"
    - "convolutional oriented boundaries"
    - "identity mappings in deep residual networks"
    - "action2activity recognizing complex activities from sensor data"
    - "holistically nested edge detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "encoding cnn activations for writer recognition"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "identity mappings in deep residual networks"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "interferences in match kernels"
    - "deep residual learning for image recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "generalized max pooling"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-8 (induced by source paper "unsupervised feature learning for writer identification and writer retrieval"):
  with members:
    - "identity mappings in deep residual networks"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "deep residual learning for image recognition"
    - "sparse radial sampling lbp for writer identification"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"
    - "cnn features off the shelf an astounding baseline for 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)