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: "automatic discovery association estimation and learning of semantic attributes for a thousand categories"
  Abstract: Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal regarding recognition performance, and most importantly, it limits the applicability of attribute-based models to large scale data sets. To tackle this problem, we propose an end-to-end unsupervised attribute learning approach. We utilize online text corpora to automatically discover a salient and discriminative vocabulary that correlates well with the human concept of semantic attributes. Moreover, we propose a deep convolutional model to optimize class-attribute associations with a linguistic prior that accounts for noise and missing data in text. In a thorough evaluation on ImageNet, we demonstrate that our model is able to efficiently discover and learn semantic attributes at a large scale. Furthermore, we demonstrate that our model outperforms the state-of-the-art in zero-shot learning on three data sets: ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable attribute-based learning on ImageNet and will share the attributes and associations for future research.

Connected through hyperedge HE-1 (induced by source paper "any shot object detection"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few example object detection with model communication"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"

Connected through hyperedge HE-2 (induced by source paper "polarity loss for zero shot object detection"):
  with members:
    - "zero shot detection"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "deep feature pyramid reconfiguration for object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-3 (induced by source paper "zero annotation object detection with web knowledge transfer"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "exploiting web images for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "adversarial discriminative domain adaptation"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

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)