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: "stfcn spatio temporal fcn for semantic video segmentation"
  Abstract: This paper presents a novel method to involve both spatial and temporal features for semantic video segmentation. Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task. We investigate how involving temporal features also has a good effect on segmenting video data. We propose a module based on a long short-term memory (LSTM) architecture of a recurrent neural network for interpreting the temporal characteristics of video frames over time. Our system takes as input frames of a video and produces a correspondingly-sized output; for segmenting the video our method combines the use of three components: First, the regional spatial features of frames are extracted using a CNN; then, using LSTM the temporal features are added; finally, by deconvolving the spatio-temporal features we produce pixel-wise predictions. Our key insight is to build spatio-temporal convolutional networks (spatio-temporal CNNs) that have an end-to-end architecture for semantic video segmentation. We adapted fully some known convolutional network architectures (such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets.

Connected through hyperedge HE-1 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "every frame counts joint learning of video segmentation and optical flow"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "selflow self supervised learning of optical flow"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "low latency video semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"

Connected through hyperedge HE-3 (induced by source paper "exploiting temporality for semi supervised video segmentation"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "youtube vos a large scale video object segmentation benchmark"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning video object segmentation with visual memory"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "low latency video semantic segmentation"):
  with members:
    - "semantic video cnns through representation warping"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "learning video object segmentation from static images"
    - "video scene parsing with predictive feature learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "optical flow with semantic segmentation and localized layers"
    - "convolutional random walk networks for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "attention to scale scale aware semantic image segmentation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "3 d deep learning approach for remote sensing image classification"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "densely connected convolutional networks"
    - "unifying adversarial training algorithms with flexible deep data gradient regularization"
    - "eie efficient inference engine on compressed deep neural network"
    - "wide residual networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "flow guided feature aggregation for video object detection"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deformable convolutional networks"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "object detection from video tubelets with convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-8 (induced by source paper "automatic real time background cut for portrait videos"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "unsupervised video segmentation via spatio temporally nonlocal appearance learning"
    - "recurrent fully convolutional networks for video segmentation"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "pyramid scene parsing network"

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)