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 patch learning for weakly supervised object classification and discovery"
  Abstract: Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background. Many existing methods usually require fine-grained supervisions (e.g., bounding-box annotations) to learn patch features, which requires a great effort to label images may limit their potential applications. In this paper, we propose to learn patch features via weak supervisions, i.e., only image-level supervisions. To achieve this goal, we treat images as bags and patches as instances to integrate the weakly supervised multiple instance learning constraints into deep neural networks. Also, our method integrates the traditional multiple stages of weakly supervised object classification and discovery into a unified deep convolutional neural network and optimizes the network in an end-to-end way. The network processes the two tasks object classification and discovery jointly, and shares hierarchical deep features. Through this jointly learning strategy, weakly supervised object classification and discovery are beneficial to each other. We test the proposed method on the challenging PASCAL VOC datasets. The results show that our method can obtain state-of-the-art performance on object classification, and very competitive results on object discovery, with faster testing speed than competitors.

Connected through hyperedge HE-1 (induced by source paper "mask scoring r cnn"):
  with members:
    - "ccnet criss cross attention for semantic segmentation"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"
    - "object detection in videos by high quality object linking"
    - "object detection in videos by short and long range object linking"
    - "segmentation aware convolutional networks using local attention masks"
    - "improving object localization with fitness nms and bounded iou loss"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "approximating cnns with bag of local features models works surprisingly well on imagenet"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "scale coding bag of deep features for human attribute and action recognition"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "hcp a flexible cnn framework for multi label image classification"
    - "bags of local convolutional features for scalable instance search"
    - "deep fishernet for object classification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "crf learning with cnn features for image segmentation"
    - "unsupervised feature learning with c svddnet"

Connected through hyperedge HE-4 (induced by source paper "pcl proposal cluster learning for weakly supervised object detection"):
  with members:
    - "self produced guidance for weakly supervised object localization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deep self taught learning for weakly supervised object localization"
    - "bayesian joint modelling for object localisation in weakly labelled images"
    - "weakly supervised object localization using things and stuff transfer"
    - "single shot object detection with enriched semantics"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-5 (induced by source paper "deep multiple instance learning for zero shot image tagging"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "multi label image classification via knowledge distillation from weakly supervised detection"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "learning deep latent spaces for multi label classification"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "automatic image annotation via label transfer in the semantic space"

Connected through hyperedge HE-7 (induced by source paper "multi label zero shot human action recognition via joint latent embedding"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "alternative semantic representations for zero shot human action recognition"
    - "transductive zero shot learning with adaptive structural embedding"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "zero shot learning the good the bad and the ugly"
    - "hcp a flexible cnn framework for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "analysis and optimization of loss functions for multiclass top k and multilabel 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)