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: "reflectance hashing for material recognition"
  Abstract: We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. #R##N#These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials.

Connected through hyperedge HE-1 (induced by source paper "photoshape photorealistic materials for large scale shape collections"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "photographic image synthesis with cascaded refinement networks"
    - "a lightweight approach for on the fly reflectance estimation"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-2 (induced by source paper "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robust imitation of diverse behaviors"
    - "learning from synthetic humans"
    - "emergence of locomotion behaviours in rich environments"
    - "how useful is photo realistic rendering for visual learning"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "differential angular imaging for material recognition"):
  with members:
    - "deep ten texture encoding network"
    - "geometry informed material recognition"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "friction from reflectance deep reflectance codes for predicting physical surface properties from one shot in field reflectance"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep ten texture encoding network"):
  with members:
    - "identity mappings in deep residual networks"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "differential angular imaging for material recognition"
    - "friction from reflectance deep reflectance codes for predicting physical surface properties from one shot in field reflectance"
    - "deep filter banks for texture recognition description and segmentation"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-5 (induced by source paper "friction from reflectance deep reflectance codes for predicting physical surface properties from one shot in field reflectance"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "material recognition in the wild with the materials in context database"
    - "matconvnet convolutional neural networks for matlab"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-6 (induced by source paper "a 4d light field dataset and cnn architectures for material recognition"):
  with members:
    - "multi view convolutional neural networks for 3d shape recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "describing textures in the wild"

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)