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: "multi label learning with missing labels using mixed dependency graphs"
  Abstract: This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels. The key point to handle missing labels is propagating the label information from provided labels to missing labels, through a dependency graph that each label of each instance is treated as a node. We build this graph by utilizing different types of label dependencies. Specifically, the instance-level similarity is served as undirected edges to connect the label nodes across different instances and the semantic label hierarchy is used as directed edges to connect different classes. This base graph is referred to as the mixed dependency graph, as it includes both undirected and directed edges. Furthermore, we present another two types of label dependencies to connect the label nodes across different classes. One is the class co-occurrence, which is also encoded as undirected edges. Combining with the base graph, we obtain a new mixed graph, called MG-CO (mixed graph with co-occurrence). The other is the sparse and low rank decomposition of the whole label matrix, to embed high-order dependencies over all labels. Combining with the base graph, the new mixed graph is called as MG-SL (mixed graph with sparse and low rank decomposition). Based on MG-CO and MG-SL, we propose two convex transductive formulations of the MLML problem, denoted as MLMG-CO and MLMG-SL, respectively. Two important applications, including image annotation and tag based image retrieval, can be jointly handled using our proposed methods. Experiments on benchmark datasets show that our methods give significant improvements in performance and robustness to missing labels over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial encoder multi task decoder for multi stage processes"):
  with members:
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "semi supervised learning with deep generative models"
    - "efficient estimation of word representations in vector space"
    - "multi stage classifier design"

Connected through hyperedge HE-2 (induced by source paper "partial multi label learning with label and feature collaboration"):
  with members:
    - "multi label learning with global and local label correlation"

Connected through hyperedge HE-3 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"
    - "recurrent attentional reinforcement learning for multi label image recognition"

Connected through hyperedge HE-4 (induced by source paper "tencent ml images a large scale multi label image database for visual representation learning"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"
    - "spatial memory for context reasoning in object detection"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "identity mappings in deep residual networks"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "tagging like humans diverse and distinct image annotation"):
  with members:
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "improving pairwise ranking for multi label image classification"
    - "annotation order matters recurrent image annotator for arbitrary length image tagging"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "nonlocal neural networks nonlocal diffusion and nonlocal modeling"):
  with members:
    - "non local neural networks"
    - "identity mappings in deep residual networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)