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: "synthcam3d semantic understanding with synthetic indoor scenes"
  Abstract: We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results.

Connected through hyperedge HE-1 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "weak supervision for generating pixel level annotations in scene text segmentation"):
  with members:
    - "coco_ts dataset pixel level annotations based on weak supervision for scene text segmentation"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "dual attention network for scene segmentation"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (induced by source paper "coco_ts dataset pixel level annotations based on weak supervision for scene text segmentation"):
  with members:
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "synthetic data for text localisation in natural images"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "annotation free and one shot learning for instance segmentation of homogeneous object clusters"):
  with members:
    - "mask r cnn"
    - "rendergan generating realistic labeled data"
    - "image to image translation with conditional adversarial networks"
    - "fully convolutional instance aware semantic segmentation"
    - "semantic pose using deep networks trained on synthetic rgb d"
    - "instance aware semantic segmentation via multi task network cascades"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "leveraging synthetic imagery for collision at sea avoidance"):
  with members:
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "procedural generation of videos to train deep action recognition networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "procedural generation of videos to train deep action recognition networks"):
  with members:
    - "how useful is photo realistic rendering for visual learning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "play and learn using video games to train computer vision models"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "learning physical intuition of block towers by example"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-7 (induced by source paper "from virtual to real world visual perception using domain adaptation the dpm as example"):
  with members:
    - "how useful is photo realistic rendering for visual learning"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "model driven simulations for deep convolutional neural networks"
    - "semantic pose using deep networks trained on synthetic rgb d"

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)