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: "weakly supervised patchnets describing and aggregating local patches for scene recognition"
  Abstract: Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called  PatchNet . PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called  VSAD , by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-2 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "semantic aware scene recognition"):
  with members:
    - "auto reid searching for a part aware convnet for person re identification"
    - "rethinking imagenet pre training"
    - "deep frank wolfe for neural network optimization"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "in pixels we trust from pixel labeling to object localization and scene categorization"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "fosnet an end to end trainable deep neural network for scene recognition"):
  with members:
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "partial convolution based padding"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "a robust indoor scene recognition method based on sparse representation"
    - "learnable pooling with context gating for video classification"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "learning effective rgb d representations for scene recognition"):
  with members:
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-6 (induced by source paper "centroid based scene classification cbsc using deep features and clustering for rgb d indoor scene classification"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "clustering with deep learning taxonomy and new methods"
    - "joint unsupervised learning of deep representations and image clusters"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "understand scene categories by objects a semantic regularized scene classifier using convolutional neural networks"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "cbcl brain inspired model for rgb d indoor scene classification"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "clustering with deep learning taxonomy and new methods"
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "the mythos of model interpretability"
    - "joint unsupervised learning of deep representations and image clusters"
    - "understand scene categories by objects a semantic regularized scene classifier using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "indoor image representation by high level semantic features"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "hierarchical deep learning architecture for 10k objects classification"
    - "rethinking the inception architecture for computer vision"
    - "multi scale orderless pooling of deep convolutional activation features"

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)