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: "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
  Abstract: We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene. Particularly in indoor videos such as captured by robotic platforms or handheld and bodyworn RGBD cameras, nearby video frames provide diverse viewpoints and additional context of objects and scenes. To leverage such information, we first compute region correspondences by optical flow and image boundary-based superpixels. Given these region correspondences, we propose a novel spatio-temporal pooling layer to aggregate information over space and time. We evaluate our approach on the NYU--Depth--V2 and the SUN3D datasets and compare it to various state-of-the-art single-view and multi-view approaches. Besides a general improvement over the state-of-the-art, we also show the benefits of making use of unlabeled frames during training for multi-view as well as single-view prediction.

Connected through hyperedge HE-1 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-2 (induced by source paper "pattern affinitive propagation across depth surface normal and semantic segmentation"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-3 (induced by source paper "segmentations leak membership inference attacks and defenses in semantic image segmentation"):
  with members:
    - "disentangling adversarial robustness and generalization"
    - "knockoff nets stealing functionality of black box models"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "unified perceptual parsing for scene understanding"
    - "privacy preserving machine learning through data obfuscation"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-4 (induced by source paper "scenecode monocular dense semantic reconstruction using learned encoded scene representations"):
  with members:
    - "fusion volumetric object level slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "futuremapping the computational structure of spatial ai systems"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-5 (induced by source paper "object segmentation tracking from generic video cues"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast online object tracking and segmentation a unifying approach"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "acnet attention based network to exploit complementary features for rgbd semantic segmentation"):
  with members:
    - "depth aware cnn for rgb d segmentation"
    - "image super resolution using very deep residual channel attention networks"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "depth aware cnn for rgb d segmentation"):
  with members:
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "deformable convolutional networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-8 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "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"

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)