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: "weakly supervised learning of objects attributes and their associations"
  Abstract: When humans describe images they tend to use combinations of nouns and adjectives, corresponding to objects and their associated attributes respectively. To generate such a description automatically, one needs to model objects, attributes and their associations. Conventional methods require strong annotation of object and attribute locations, making them less scalable. In this paper, we model object-attribute associations from weakly labelled images, such as those widely available on media sharing sites (e.g. Flickr), where only image-level labels (either object or attributes) are given, without their locations and associations. This is achieved by introducing a novel weakly supervised non-parametric Bayesian model. Once learned, given a new image, our model can describe the image, including objects, attributes and their associations, as well as their locations and segmentation. Extensive experiments on benchmark datasets demonstrate that our weakly supervised model performs at par with strongly supervised models on tasks such as image description and retrieval based on object-attribute associations.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised image annotation and segmentation with objects and attributes"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "vqa visual question answering"
    - "natural language object retrieval"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "deep visual semantic alignments for generating image descriptions"
    - "fully convolutional multi class multiple instance learning"
    - "show and tell a neural image caption generator"
    - "from image level to pixel level labeling with convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "transferring a semantic representation for person re identification and search"):
  with members:
    - "clothing co parsing by joint image segmentation and labeling"
    - "neural codes for image retrieval"
    - "human re identification by matching compositional template with cluster sampling"

Connected through hyperedge HE-3 (induced by source paper "learning attributes equals multi source domain generalization"):
  with members:
    - "learning hypergraph regularized attribute predictors"
    - "automatic concept discovery from parallel text and visual corpora"
    - "evaluation of output embeddings for fine grained image classification"
    - "domain generalization for object recognition with multi task autoencoders"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "a unified semantic embedding relating taxonomies and attributes"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised learning of heterogeneous concepts in videos"):
  with members:
    - "the long short story of movie description"
    - "automatic concept discovery from parallel text and visual corpora"
    - "sequence to sequence video to text"
    - "weakly supervised alignment of video with text"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "describing multimedia content using attention based encoder decoder networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "from captions to visual concepts and back"

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)