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: "geometry informed material recognition"
  Abstract: Our goal is to recognize material categories using images and geometry information. In many applications, such as construction management, coarse geometry information is available. We investigate how 3D geometry (surface normals, camera intrinsic and extrinsic parameters) can be used with 2D features (texture and color) to improve material classification. We introduce a new dataset, GeoMat, which is the first to provide both image and geometry data in the form of: (i) training and testing patches that were extracted at different scales and perspectives from real world examples of each material category, and (ii) a large scale construction site scene that includes 160 images and over 800,000 hand labeled 3D points. Our results show that using 2D and 3D features both jointly and independently to model materials improves classification accuracy across multiple scales and viewing directions for both material patches and images of a large scale construction site scene.

Connected through hyperedge HE-1 (induced by source paper "material based object tracking in hyperspectral videos benchmark and algorithms"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "object tracking in hyperspectral videos with convolutional features and kernelized correlation filter"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"):
  with members:
    - "weakly supervised dcnn for rgb d object recognition in real world applications which lack large scale annotated training data"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "material recognition from local appearance in global context"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "conditional random fields as recurrent neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"

Connected through hyperedge HE-3 (induced by source paper "differential angular imaging for material recognition"):
  with members:
    - "deep ten texture encoding network"
    - "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"
    - "reflectance hashing for material recognition"

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)