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: "joint progressive learning from high dimensional data for multi label classification"
  Abstract: Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image 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-2 (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"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "building instance classification using street view images"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "morphological filtering in shape spaces applications using tree based image representations"

Connected through hyperedge HE-3 (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"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "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-4 (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"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "statistical analysis and parameter selection for mapper"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-5 (induced by source paper "fourier based rotation invariant feature boosting an efficient framework for geospatial object detection"):
  with members:
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "a survey on object detection in optical remote sensing images"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"):
  with members:
    - "dynamical spectral unmixing of multitemporal hyperspectral images"
    - "structured sparse method for hyperspectral unmixing"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-7 (induced by source paper "cospace common subspace learning from hyperspectral multispectral correspondences"):
  with members:
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "dynamical spectral unmixing of multitemporal hyperspectral images"

Connected through hyperedge HE-8 (induced by source paper "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"):
  with members:
    - "building instance classification using street view images"
    - "robust recovery of subspace structures by low rank representation"

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.LG(Machine Learning)