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: "improving multi label learning with missing labels by structured semantic correlations"
  Abstract: Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing works on the problem either ignore the label-label and instance-instance correlations or just assume these correlations are linear and unstructured. Considering that semantic correlations between images are actually structured, in this paper we propose to incorporate structured semantic correlations to solve the missing label problem of multi-label learning. Specifically, we project images to the semantic space with an effective semantic descriptor. A semantic graph is then constructed on these images to capture the structured correlations between them. We utilize the semantic graph Laplacian as a smooth term in the multi-label learning formulation to incorporate the structured semantic correlations. Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations. We achieve better results than state-of-the-art multi-label learning methods on four benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "learning a deep convnet for multi label classification with partial labels"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "webvision challenge visual learning and understanding with web data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "multi label zero shot learning with transfer aware label embedding projection"):
  with members:
    - "multi label zero shot learning with structured knowledge graphs"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "fast zero shot image tagging"
    - "cnn rnn a unified framework for multi label image classification"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-4 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "real time action recognition with enhanced motion vector cnns"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "places an image database for deep scene understanding"
    - "weakly supervised fine grained categorization with part based image representation"

Connected through hyperedge HE-5 (induced by source paper "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"):
  with members:
    - "identity mappings in deep residual networks"
    - "yfcc100m the new data in multimedia research"
    - "weakly supervised deep detection networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "exploit bounding box annotations for multi label object recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "from facial expression recognition to interpersonal relation prediction"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "grid loss detecting occluded faces"
    - "sparsifying neural network connections for face recognition"
    - "do deep neural networks learn facial action units when doing expression recognition"

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)