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: "subspace support vector data description"
  Abstract: This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.

Connected through hyperedge HE-1 (induced by source paper "boosting rare benthic macroinvertebrates taxa identification with one class classification"):
  with members:
    - "automatic image based identification and biomass estimation of invertebrates"
    - "multimodal subspace support vector data description"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "gods generalized one class discriminative subspaces for anomaly detection"):
  with members:
    - "learning deep features for one class classification"
    - "learning discriminative video representations using adversarial perturbations"
    - "anomaly detection using one class neural networks"
    - "video representation learning using discriminative pooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "one class slab support vector machine"

Connected through hyperedge HE-3 (induced by source paper "multimodal subspace support vector data description"):
  with members:
    - "weighted linear discriminant analysis based on class saliency information"
    - "vehicle tracking using surveillance with multimodal data fusion"
    - "multimodal machine learning a survey and taxonomy"
    - "captioning images with diverse objects"
    - "a survey on multi view learning"

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)