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: "deeply supervised salient object detection with short connections"
  Abstract: Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and salient object detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. The Holistically-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious. In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms. Beyond that, we conduct an exhaustive analysis of the role of training data on performance. We provide a training set for future research and fair comparisons.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "egnet edge guidance network for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"

Connected through hyperedge HE-3 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-4 (induced by source paper "cross layer feature pyramid network for salient object detection"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-5 (induced by source paper "controlling generative models with continuous factors of variations"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "inverting the generator of a generative adversarial network ii"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "interaction graphs for object importance estimation in on road driving videos"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "graph r cnn for scene graph generation"
    - "videos as space time region graphs"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "predicting driver attention in critical situations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-7 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "non local neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-8 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "deep semantic segmentation of natural and medical images a review"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "robuststl a robust seasonal trend decomposition algorithm for long time series"

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)