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: "hierarchical bilinear pooling for fine grained visual recognition"
  Abstract: Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling based approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "dropblock a regularization method for convolutional networks"
    - "learning to navigate for fine grained classification"
    - "low rank bilinear pooling for fine grained classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"

Connected through hyperedge HE-2 (induced by source paper "channel interaction networks for fine grained image categorization"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "compact generalized non local network"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "non local neural networks"
    - "attention is all you need"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-4 (induced by source paper "w posenet dense correspondence regularized pixel pair pose regression"):
  with members:
    - "6d pose estimation with correlation fusion"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "grasp pose detection in point clouds"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-5 (induced by source paper "fine grained visual recognition with batch confusion norm"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "object part attention model for fine grained image classification"
    - "learning to navigate for fine grained classification"
    - "free form image inpainting with gated convolution"
    - "improved bilinear pooling with cnns"
    - "pairwise confusion for fine grained visual classification"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-6 (induced by source paper "low rank random tensor for bilinear pooling"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "local temporal bilinear pooling for fine grained action parsing"
    - "bilinear attention networks"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "mutan multimodal tucker fusion for visual question answering"
    - "is second order information helpful for large scale visual recognition"
    - "attentional pooling for action recognition"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "zero shot learning with sparse attribute propagation"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot kernel learning"
    - "learning from noisy web data with category level supervision"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "transductive zero shot learning with a self training dictionary approach"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-8 (induced by source paper "local temporal bilinear pooling for fine grained action parsing"):
  with members:
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "compact generalized non local network"
    - "temporal human action segmentation via dynamic clustering"
    - "monet moments embedding network"
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "statistically motivated second order pooling"
    - "part aligned bilinear representations for person re identification"

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)