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: "semantic pose using deep networks trained on synthetic rgb d"
  Abstract: In this work we address the problem of indoor scene understanding from RGB-D images. Specifically, we propose to find instances of common furniture classes, their spatial extent, and their pose with respect to generalized class models. To accomplish this, we use a deep, wide, multi-output convolutional neural network (CNN) that predicts class, pose, and location of possible objects simultaneously. To overcome the lack of large annotated RGB-D training sets (especially those with pose), we use an on-the-fly rendering pipeline that generates realistic cluttered room scenes in parallel to training. We then perform transfer learning on the relatively small amount of publicly available annotated RGB-D data, and find that our model is able to successfully annotate even highly challenging real scenes. Importantly, our trained network is able to understand noisy and sparse observations of highly cluttered scenes with a remarkable degree of accuracy, inferring class and pose from a very limited set of cues. Additionally, our neural network is only moderately deep and computes class, pose and position in tandem, so the overall run-time is significantly faster than existing methods, estimating all output parameters simultaneously in parallel on a GPU in seconds.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "robust place categorization with deep domain generalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-4 (induced by source paper "normalized object coordinate space for category level 6d object pose and size estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "sim to real learning for casualty detection from ground projected point cloud data"):
  with members:
    - "casualty detection from 3d point cloud data for autonomous ground mobile rescue robots"
    - "resqbot a mobile rescue robot with immersive teleperception for casualty extraction"
    - "focal loss for dense object detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "megdet a large mini batch object detector"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "spigan privileged adversarial learning from simulation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "modality distillation with multiple stream networks for action recognition"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-7 (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"
    - "instance aware semantic segmentation via multi task network cascades"
    - "synthcam3d semantic understanding with synthetic indoor scenes"
    - "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-8 (induced by source paper "sim4cv a photo realistic simulator for computer vision applications"):
  with members:
    - "carla an open urban driving simulator"
    - "end to end learning for self driving cars"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "asynchronous methods for deep reinforcement learning"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning physical intuition of block towers by example"
    - "playing for data ground truth from computer games"
    - "procedural generation of videos to train deep action recognition networks"

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)