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: "a large dataset of object scans"
  Abstract: We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their choosing, outside the laboratory and without direct supervision by computer vision professionals. The result is a large and diverse collection of object scans: from shoes, mugs, and toys to grand pianos, construction vehicles, and large outdoor sculptures. We worked with an attorney to ensure that data acquisition did not violate privacy constraints. The acquired data was placed irrevocably in the public domain and is available freely at http://redwood-data.org/3dscan.

Connected through hyperedge HE-1 (induced by source paper "deep non line of sight reconstruction"):
  with members:
    - "non line of sight reconstruction using efficient transient rendering"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "non line of sight imaging with partial occluders and surface normals"
    - "learning representations and generative models for 3d point clouds"
    - "fast back projection for non line of sight reconstruction"
    - "convolutional pose machines"
    - "learning a predictable and generative vector representation for objects"

Connected through hyperedge HE-2 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data"):
  with members:
    - "city scale road extraction from satellite imagery v2 road speeds and travel times"
    - "spacenet mvoi a multi view overhead imagery dataset"
    - "spacenet a remote sensing dataset and challenge series"
    - "from satellite imagery to disaster insights"
    - "deep learning in agriculture a survey"
    - "benchmark dataset for automatic damaged building detection from post hurricane remotely sensed imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "from planes to corners multi purpose primitive detection in unorganized 3d point clouds"):
  with members:
    - "generic primitive detection in point clouds using novel minimal quadric fits"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "a minimalist approach to type agnostic detection of quadrics in point clouds"
    - "cad priors for accurate and flexible instance reconstruction"

Connected through hyperedge HE-4 (induced by source paper "dist rendering deep implicit signed distance function with differentiable sphere tracing"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-5 (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"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-6 (induced by source paper "revisiting point cloud classification a new benchmark dataset and classification model on real world data"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"
    - "learning to drive from simulation without real world labels"

Connected through hyperedge HE-7 (induced by source paper "photometric mesh optimization for video aligned 3d object reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "proxy templates for inverse compositional photometric bundle adjustment"
    - "unsupervised learning of depth and ego motion from video"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-8 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "spatially transformed adversarial examples"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"

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)