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: "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
  Abstract: Multi-instance multi-label (MIML) learning has many interesting applications in computer visions, including multi-object recognition and automatic image tagging. In these applications, additional information such as bounding-boxes, image captions and descriptions is often available during training phrase, which is referred as privileged information (PI). However, as existing works on learning using PI only consider instance-level PI (privileged instances), they fail to make use of bag-level PI (privileged bags) available in MIML learning. Therefore, in this paper, we propose a two-stream fully convolutional network, named MIML-FCN+, unified by a novel PI loss to solve the problem of MIML learning with privileged bags. Compared to the previous works on PI, the proposed MIML-FCN+ utilizes the readily available privileged bags, instead of hard-to-obtain privileged instances, making the system more general and practical in real world applications. As the proposed PI loss is convex and SGD compatible and the framework itself is a fully convolutional network, MIML-FCN+ can be easily integrated with state of-the-art deep learning networks. Moreover, the flexibility of convolutional layers allows us to exploit structured correlations among instances to facilitate more effective training and testing. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed MIML-FCN+, outperforming state-of-the-art methods in the application of multi-object recognition.

Connected through hyperedge HE-1 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-2 (induced by source paper "g softmax improving intra class compactness and inter class separability of features"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "dilated residual networks"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "densely connected convolutional networks"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-3 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "soft sampling for robust object detection"
    - "exploiting web images for weakly supervised object detection"
    - "soft nms improving object detection with one line of code"
    - "zero annotation object detection with web knowledge transfer"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised person re identification"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "attention aware compositional network for person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "dynamic label graph matching for unsupervised video re identification"
    - "unsupervised person re identification clustering and fine tuning"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "learning to separate object sounds by watching unlabeled video"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"

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"
    - "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"
    - "deep patch learning for weakly supervised object classification and discovery"

Connected through hyperedge HE-7 (induced by source paper "fine grained classification using heterogeneous web data and auxiliary categories"):
  with members:
    - "zero shot kernel learning"
    - "learning from noisy web data with category level supervision"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "zero shot learning the good the bad and the ugly"
    - "semi supervised zero shot learning by a clustering based approach"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "places an image database for deep scene understanding"

Connected through hyperedge HE-8 (induced by source paper "graph distillation for action detection with privileged modalities"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "temporal action detection with structured segment networks"
    - "investigation of different skeleton features for cnn based 3d action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "joint distance maps based action recognition with convolutional neural networks"

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)