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: "material recognition in the wild with the materials in context database"
  Abstract: Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. #R##N#MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds"):
  with members:
    - "panoptic segmentation"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "surface edge explorer see planning next best views directly from 3d observations"
    - "dynamic graph cnn for learning on point clouds"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning ground traversability from simulations"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-3 (induced by source paper "histogram layers for texture analysis"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "deep texture manifold for ground terrain recognition"
    - "texture classification in extreme scale variations using ganet"
    - "deep ten texture encoding network"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "three dimensional convolutional neural network 3d cnn for heterogeneous material homogenization"):
  with members:
    - "advanced steel microstructural classification by deep learning methods"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning for surface material classification using haptic and visual information"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "learning human object interactions by graph parsing neural networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-6 (induced by source paper "material segmentation of multi view satellite imagery"):
  with members:
    - "context encoding for semantic segmentation"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "stacked hourglass networks for human pose estimation"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "deep ten texture encoding network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "revisiting temporal modeling for video based person reid"
    - "gaussian material synthesis"
    - "a lightweight approach for on the fly reflectance estimation"

Connected through hyperedge HE-8 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"

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)