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: "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
  Abstract: Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity and atmospheric effects) and instrumental configurations (e.g., sensor noise), and material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with the previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"):
  with members:
    - "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"
    - "relation network for multi label aerial image classification"
    - "so2sat lcz42 a benchmark dataset for global local climate zones classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "mapping missing population in rural india a deep learning approach with satellite imagery"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"

Connected through hyperedge HE-2 (induced by source paper "feature extraction for hyperspectral imagery the evolution from shallow to deep"):
  with members:
    - "simultaneous spectral spatial feature selection and extraction for hyperspectral images"
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"

Connected through hyperedge HE-3 (induced by source paper "invariant attribute profiles a spatial frequency joint feature extractor for hyperspectral image classification"):
  with members:
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "building instance classification using street view images"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "morphological filtering in shape spaces applications using tree based image representations"

Connected through hyperedge HE-4 (induced by source paper "classification of hyperspectral and lidar data using coupled cnns"):
  with members:
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "learning shared cross modality representation using multispectral lidar and hyperspectral data"):
  with members:
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"

Connected through hyperedge HE-6 (induced by source paper "deep clustering with intra class distance constraint for hyperspectral images"):
  with members:
    - "trace quotient with sparsity priors for learning low dimensional image representations"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "unsupervised deep embedding for clustering analysis"
    - "convolutional clustering for unsupervised learning"

Connected through hyperedge HE-7 (induced by source paper "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"):
  with members:
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "a survey on object detection in optical remote sensing images"
    - "dynamical spectral unmixing of multitemporal hyperspectral images"
    - "yolo9000 better faster stronger"
    - "aggregate channel features for multi view face detection"

Connected through hyperedge HE-8 (induced by source paper "mima mapper induced manifold alignment for semi supervised fusion of optical image and polarimetric sar data"):
  with members:
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "statistical analysis and parameter selection for mapper"
    - "cascaded recurrent neural networks for hyperspectral 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)