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 label prediction based semi supervised adaptive concept factorization for robust data representation"
  Abstract: Constrained Concept Factorization (CCF) yields the enhanced representation ability over CF by incorporating label information as additional constraints, but it cannot classify and group unlabeled data appropriately. Minimizing the difference between the original data and its reconstruction directly can enable CCF to model a small noisy perturbation, but is not robust to gross sparse errors. Besides, CCF cannot preserve the manifold structures in new representation space explicitly, especially in an adaptive manner. In this paper, we propose a joint label prediction based Robust Semi-Supervised Adaptive Concept Factorization (RS2ACF) framework. To obtain robust representation, RS2ACF relaxes the factorization to make it simultaneously stable to small entrywise noise and robust to sparse errors. To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data. By the label indicator, RS2ACF can ensure the unlabeled data of the same predicted label to be mapped into the same class in feature space. Besides, RS2ACF incorporates the joint neighborhood reconstruction error over the new representations and predicted labels of both labeled and unlabeled data, so the manifold structures can be preserved explicitly and adaptively in the representation space and label space at the same time. Owing to the adaptive manner, the tricky process of determining the neighborhood size or kernel width can be avoided. Extensive results on public databases verify that our RS2ACF can deliver state-of-the-art data representation, compared with other related methods.

Connected through hyperedge HE-1 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space"):
  with members:
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-2 (induced by source paper "multi view subspace clustering via partition fusion"):
  with members:
    - "robust graph learning from noisy data"
    - "robust subspace clustering with compressed data"
    - "multi view multiple clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "exploring graph learning for semi supervised classification beyond euclidean data"
    - "multiple partitions aligned clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "unified spectral clustering with optimal graph"

Connected through hyperedge HE-3 (induced by source paper "clustering aware graph construction a joint learning perspective"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "graph learning from data under laplacian and structural constraints"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-4 (induced by source paper "multilayer collaborative low rank coding network for robust deep subspace discovery"):
  with members:
    - "robust graph learning from noisy data"
    - "robust subspace clustering with compressed data"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "robust triple matrix recovery based auto weighted label propagation for classification"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "adaptive structure constrained robust latent low rank coding for image recovery"
    - "subspace clustering by block diagonal representation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "deep self representative concept factorization network for representation learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "the trustworthy pal controlling the false discovery rate in boolean matrix factorization"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "did distributed incremental block coordinate descent for nonnegative matrix factorization"
    - "latitude a model for mixed linear tropical matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "discriminative local sparse representation by robust adaptive dictionary pair learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "kernel induced label propagation by mapping for semi supervised classification"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-7 (induced by source paper "robust subspace discovery by block diagonal adaptive locality constrained representation"):
  with members:
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-8 (induced by source paper "adaptive structure constrained robust latent low rank coding for image recovery"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "object classification with joint projection and low rank dictionary learning"
    - "fast low rank matrix learning with nonconvex regularization"
    - "making a science of model search"
    - "robust principal component analysis"

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)