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: "learning in high dimensional multimedia data the state of the art"
  Abstract: During the last decade, the deluge of multimedia data has impacted a wide range of research areas, including multimedia retrieval, 3D tracking, database management, data mining, machine learning, social media analysis, medical imaging, and so on. Machine learning is largely involved in multimedia applications of building models for classification and regression tasks etc., and the learning principle consists in designing the models based on the information contained in the multimedia dataset. While many paradigms exist and are widely used in the context of machine learning, most of them suffer from the `curse of dimensionality', which means that some strange phenomena appears when data are represented in a high-dimensional space. Given the high dimensionality and the high complexity of multimedia data, it is important to investigate new machine learning algorithms to facilitate multimedia data analysis. To deal with the impact of high dimensionality, an intuitive way is to reduce the dimensionality. On the other hand, some researchers devoted themselves to designing some effective learning schemes for high-dimensional data. In this survey, we cover feature transformation, feature selection and feature encoding, three approaches fighting the consequences of the curse of dimensionality. Next, we briefly introduce some recent progress of effective learning algorithms. Finally, promising future trends on multimedia learning are envisaged.

Connected through hyperedge HE-1 (induced by source paper "application of fuzzy clustering for text data dimensionality reduction"):
  with members:
    - "twitter speaks a case of national disaster situational awareness"
    - "characterizing transgender health issues in twitter"
    - "what do the us west coast public libraries post on twitter"

Connected through hyperedge HE-2 (induced by source paper "exponential discriminative metric embedding in deep learning"):
  with members:
    - "binary generative adversarial networks for image retrieval"
    - "funnel structured cascade for multi view face detection with alignment awareness"
    - "a survey on learning to hash"
    - "wide residual networks"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"
    - "unconstrained face verification using deep cnn features"

Connected through hyperedge HE-3 (induced by source paper "generalized zero shot learning for action recognition with web scale video data"):
  with members:
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "zero shot learning the good the bad and the ugly"
    - "real time action recognition with enhanced motion vector cnns"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-4 (induced by source paper "relational autoencoder for feature extraction"):
  with members:
    - "deep feature learning for eeg recordings"
    - "autoencoding the retrieval relevance of medical images"

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)