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 imbalanced attribute classification using visual attention aggregation"
  Abstract: For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods follow either a computer vision approach while failing to account for class imbalance, or explore machine learning solutions, which disregard the spatial and semantic relations that exist in the images. With that in mind, we propose an effective method that extracts and aggregates visual attention masks at different scales. We introduce a loss function to handle class imbalance both at class and at an instance level and further demonstrate that penalizing attention masks with high prediction variance accounts for the weak supervision of the attention mechanism. By identifying and addressing these challenges, we achieve state-of-the-art results with a simple attention mechanism in both PETA and WIDER-Attribute datasets without additional context or side information.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "psdf fusion probabilistic signed distance function for on the fly 3d data fusion and scene reconstruction"

Connected through hyperedge HE-2 (induced by source paper "adversarial representation learning for text to image matching"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "mirrorgan learning text to image generation by redescription"
    - "in defense of the triplet loss for visual recognition"
    - "boosting standard classification architectures through a ranking regularizer"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-3 (induced by source paper "on symbiosis of attribute prediction and semantic segmentation"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "improving facial attribute prediction using semantic segmentation"
    - "class rectification hard mining for imbalanced deep learning"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "rethinking atrous convolution for semantic image segmentation"
    - "semantic object parsing with graph lstm"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "data augmentation for object detection via progressive and selective instance switching"):
  with members:
    - "data augmentation using random image cropping and patching for deep cnns"
    - "dynamic curriculum learning for imbalanced data classification"
    - "class balanced loss based on effective number of samples"
    - "learning to segment via cut and paste"
    - "data augmentation by pairing samples for images classification"
    - "on the importance of visual context for data augmentation in scene understanding"
    - "compositional gan learning conditional image composition"
    - "modeling visual context is key to augmenting object detection datasets"

Connected through hyperedge HE-5 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"
    - "neural motifs scene graph parsing with global context"

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"
    - "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"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "improving pedestrian attribute recognition with weakly supervised multi scale attribute specific localization"):
  with members:
    - "pedestrian attribute recognition a survey"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "localization guided learning for pedestrian attribute recognition"
    - "resource aware person re identification across multiple resolutions"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "squeeze and excitation networks"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-8 (induced by source paper "class balanced loss based on effective number of samples"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "feature transfer learning for deep face recognition with under represented data"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the devil is in the tails fine grained classification in the wild"
    - "focal loss for dense object detection"
    - "class rectification hard mining for imbalanced deep 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)