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: "training constrained deconvolutional networks for road scene semantic segmentation"
  Abstract: A source deconvolutional network is adaptively trained to perform semantic segmentation. Image data is then input to the source deconvolutional network and outputs of the S-Net are measured. The same image data and the measured outputs of the source deconvolutional network are then used to train a target deconvolutional network. The target deconvolutional network is defined by a substantially fewer numerical parameters than the source deconvolutional network.

Connected through hyperedge HE-1 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "yolov3 an incremental improvement"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "citypersons a diverse dataset for pedestrian detection"
    - "mot16 a benchmark for multi object tracking"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-2 (induced by source paper "ace adapting to changing environments for semantic segmentation"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "multimodal unsupervised image to image translation"
    - "reinforced continual learning"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "knowledge squeezed adversarial network compression"):
  with members:
    - "ktan knowledge transfer adversarial network"
    - "model compression via distillation and quantization"
    - "attention guided answer distillation for machine reading comprehension"
    - "learn to pay attention"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

Connected through hyperedge HE-4 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "3d manhattan room layout reconstruction from a single 360 image"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"

Connected through hyperedge HE-5 (induced by source paper "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "improving fast segmentation with teacher student learning"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "pyramid scene parsing network"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "joint coarse and fine reasoning for deep optical flow"):
  with members:
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow with semantic segmentation and localized layers"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "deep residual learning for image recognition"
    - "learning deconvolution network for semantic segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "curriculum domain adaptation for semantic segmentation of urban scenes"):
  with members:
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "harnessing deep neural networks with logic rules"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "play and learn using video games to train computer vision models"
    - "from virtual to real world visual perception using domain adaptation the dpm as example"

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)