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: "deep spatio temporal random fields for efficient video segmentation"
  Abstract: In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos.

Connected through hyperedge HE-1 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "deep video inpainting"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"

Connected through hyperedge HE-2 (induced by source paper "fast video object segmentation with spatio temporal gans"):
  with members:
    - "video to video synthesis"
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised person image synthesis in arbitrary poses"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "architecture search of dynamic cells for semantic video segmentation"):
  with members:
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"
    - "snas stochastic neural architecture search"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-4 (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"
    - "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"
    - "accel a corrective fusion network for efficient semantic segmentation on video"

Connected through hyperedge HE-5 (induced by source paper "the 2019 davis challenge on vos unsupervised multi object segmentation"):
  with members:
    - "towards segmenting everything that moves"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "improving semantic segmentation via video propagation and label relaxation"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "adaptive affinity fields for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "lucid data dreaming for video object segmentation"):
  with members:
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "super trajectory for video segmentation"
    - "guided optical flow learning"
    - "learning video object segmentation with visual memory"
    - "learning from synthetic humans"
    - "online adaptation of convolutional neural networks for video object segmentation"

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)