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: "deep convolutional ranking for multilabel image annotation"
  Abstract: Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature.

Connected through hyperedge HE-1 (induced by source paper "learn to predict sets using feed forward neural networks"):
  with members:
    - "rep the set neural networks for learning set representations"
    - "deep set prediction networks"
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "yolov3 an incremental improvement"
    - "deep sets"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"

Connected through hyperedge HE-2 (induced by source paper "context aware image annotation in active learning"):
  with members:
    - "how does tweet difficulty affect labeling performance of annotators"
    - "improving image classification with location context"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-3 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"

Connected through hyperedge HE-4 (induced by source paper "graph u nets"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "large scale learnable graph convolutional networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "predicting multicellular function through multi layer tissue networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "efficient and invariant convolutional neural networks for dense prediction"

Connected through hyperedge HE-5 (induced by source paper "towards coherent and cohesive long form text generation"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "unsupervised text style transfer using language models as discriminators"
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "hard aware fashion attribute classification"):
  with members:
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "imbalanced deep learning by minority class incremental rectification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "in defense of the triplet loss for person re identification"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-7 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-8 (induced by source paper "orderless recurrent models for multi label classification"):
  with members:
    - "elucidating image to set prediction an analysis of models losses and datasets"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "a comprehensive survey of deep learning for image captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"

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)