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: "human centric indoor scene synthesis using stochastic grammar"
  Abstract: We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed to represent indoor scenes. The S-AOG is a probabilistic grammar model, in which the terminal nodes are object entities. Human contexts as contextual relations are encoded by Markov Random Fields (MRF) on the terminal nodes. We learn the distributions from an indoor scene dataset and sample new layouts using Monte Carlo Markov Chain. Experiments demonstrate that our method can robustly sample a large variety of realistic room layouts based on three criteria: (i) visual realism comparing to a state-of-the-art room arrangement method, (ii) accuracy of the affordance maps with respect to groundtruth, and (ii) the functionality and naturalness of synthesized rooms evaluated by human subjects. The code is available at https://github.com/SiyuanQi/human-centric-scene-synthesis.

Connected through hyperedge HE-1 (induced by source paper "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction"):
  with members:
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "fast and scalable position based layout synthesis"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "grains generative recursive autoencoders for indoor scenes"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "automatic generation of constrained furniture layouts"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-2 (induced by source paper "style compatible object recommendation for multi room indoor scene synthesis"):
  with members:
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "fast and scalable position based layout synthesis"
    - "semantic scene completion from a single depth image"
    - "neural autoregressive distribution estimation"

Connected through hyperedge HE-3 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "continuous adaptation for interactive object segmentation by learning from corrections"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "task free continual learning"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "interactive video object segmentation in the wild"
    - "iteratively trained interactive segmentation"

Connected through hyperedge HE-6 (induced by source paper "hierarchy denoising recursive autoencoders for 3d scene layout prediction"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "complex yolo real time 3d object detection on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "layoutvae stochastic scene layout generation from a label set"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "grains generative recursive autoencoders for indoor scenes"

Connected through hyperedge HE-8 (induced by source paper "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "learning human object interactions by graph parsing neural networks"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning to detect human object interactions"

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)