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: "unsupervised feature learning with c svddnet"
  Abstract: In this paper, we investigate the problem of learning feature representation from unlabeled data using a single-layer K-means network. A K-means network maps the input data into a feature representation by finding the nearest centroid for each input point, which has attracted researchers' great attention recently due to its simplicity, effectiveness, and scalability. However, one drawback of this feature mapping is that it tends to be unreliable when the training data contains noise. To address this issue, we propose a SVDD based feature learning algorithm that describes the density and distribution of each cluster from K-means with an SVDD ball for more robust feature representation. For this purpose, we present a new SVDD algorithm called C-SVDD that centers the SVDD ball towards the mode of local density of each cluster, and we show that the objective of C-SVDD can be solved very efficiently as a linear programming problem. Additionally, traditional unsupervised feature learning methods usually take an average or sum of local representations to obtain global representation which ignore spatial relationship among them. To use spatial information we propose a global representation with a variant of SIFT descriptor. The architecture is also extended with multiple receptive field scales and multiple pooling sizes. Extensive experiments on several popular object recognition benchmarks, such as STL-10, MINST, Holiday and Copydays shows that the proposed C-SVDDNet method yields comparable or better performance than that of the previous state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "self supervised visual representations for cross modal retrieval"):
  with members:
    - "texttopicnet self supervised learning of visual features through embedding images on semantic text spaces"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "ambient sound provides supervision for visual learning"
    - "a comprehensive survey on cross modal retrieval"
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-2 (induced by source paper "unsupervised representation learning by discovering reliable image relations"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "deep unsupervised learning of visual similarities"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "representation learning by learning to count"

Connected through hyperedge HE-3 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "crf learning with cnn features for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "juncnet a deep neural network for road junction disambiguation for autonomous vehicles"):
  with members:
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "apac augmented pattern classification with neural networks"
    - "vision based road detection in automotive systems a real time expectation driven approach"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of visual features through embedding images into text topic spaces"):
  with members:
    - "ambient sound provides supervision for visual learning"
    - "a comprehensive survey on cross modal retrieval"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "data dependent initializations of convolutional neural networks"
    - "fast r cnn"
    - "convolutional clustering for unsupervised learning"
    - "learning to see by moving"

Connected through hyperedge HE-6 (induced by source paper "recursive autoconvolution for unsupervised learning of convolutional neural networks"):
  with members:
    - "deep convolutional neural networks as generic feature extractors"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "unsupervised learning of visual representations using videos"
    - "convolutional clustering for unsupervised learning"
    - "semi supervised learning with ladder networks"
    - "winner take all autoencoders"
    - "committees of deep feedforward networks trained with few data"
    - "convolutional kernel networks"

Connected through hyperedge HE-7 (induced by source paper "bayesian neighbourhood component analysis"):
  with members:
    - "a distributed approach toward discriminative distance metric learning"
    - "bayesian active distance metric learning"

Connected through hyperedge HE-8 (induced by source paper "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  with members:
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "inverting visual representations with convolutional networks"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "natural scene recognition based on superpixels and deep boltzmann machines"
    - "max margin based discriminative feature learning"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural 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)