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: "bilinear cnns for fine grained visual recognition"
  Abstract: We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense classification and implanting for few shot learning"
    - "generative modeling for small data object detection"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"
    - "learning to find common objects across few image collections"

Connected through hyperedge HE-2 (induced by source paper "action recognition with spatial temporal discriminative filter banks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-3 (induced by source paper "conquering the cnn over parameterization dilemma a volterra filtering approach for action recognition"):
  with members:
    - "human action recognition and prediction a survey"
    - "non linear convolution filters for cnn based learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "image to image translation with conditional adversarial networks"
    - "convolutional two stream network fusion for video action recognition"
    - "deep temporal linear encoding networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "diversity regularized spatiotemporal attention for video based person re identification"):
  with members:
    - "video based person re identification with accumulative motion context"
    - "identity aware textual visual matching with latent co attention"
    - "person search with natural language description"
    - "quality aware network for set to set recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "a structured self attentive sentence embedding"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-5 (induced by source paper "deepkspd learning kernel matrix based spd representation for fine grained image recognition"):
  with members:
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "bregman divergences for infinite dimensional covariance matrices"
    - "convolutional kernel networks"

Connected through hyperedge HE-6 (induced by source paper "fine to coarse knowledge transfer for low res image classification"):
  with members:
    - "bilinear cnn models for fine grained visual recognition"
    - "part based r cnns for fine grained category detection"
    - "imagenet large scale visual recognition challenge"
    - "learning deep object detectors from 3d models"
    - "fine grained visual classification of aircraft"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "spatial transformer networks"):
  with members:
    - "contextual action recognition with r cnn"
    - "bilinear cnn models for fine grained visual recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "deep image scaling up image recognition"):
  with members:
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "part based r cnns for fine grained category detection"

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)