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: "a unified view of multi label performance measures"
  Abstract: Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algorithms perform well on which measure(s) and why. In this paper, we propose a unified margin view to revisit eleven performance measures in multi-label classification. In particular, we define label-wise margin and instance-wise margin, and prove that through maximizing these margins, different corresponding performance measures will be optimized. Based on the defined margins, a max-margin approach called LIMO is designed and empirical results verify our theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "joint ranking svm and binary relevance with robust low rank learning for multi label classification"):
  with members:
    - "multi label learning with global and local label correlation"
    - "cnn rnn a unified framework for multi label image classification"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-2 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "label refinery improving imagenet classification through label progression"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "relation network for multi label aerial image classification"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "recent advances and opportunities in scene classification of aerial images with deep models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"

Connected through hyperedge HE-4 (induced by source paper "multi label adversarial perturbations"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial attacks on neural network policies"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "learning multi level region consistency with dense multi label networks for semantic segmentation"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "adversarial examples for evaluating reading comprehension systems"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-5 (induced by source paper "the imaterialist fashion attribute dataset"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "webvision database visual learning and understanding from web data"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-6 (induced by source paper "iiitm face a database for facial attribute detection in constrained and simulated unconstrained environments"):
  with members:
    - "fairface face attribute dataset for balanced race gender and age"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "the indian spontaneous expression database for emotion recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "umdfaces an annotated face dataset for training deep networks"
    - "deep residual learning for image recognition"
    - "no more pesky learning rate guessing games"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "multi label learning with deep forest"):
  with members:
    - "distributed deep forest and its application to automatic detection of cash out fraud"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "autoencoder by forest"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "large scale multi label text classification revisiting neural networks"
    - "top down induction of clustering trees"

Connected through hyperedge HE-8 (induced by source paper "self paced multi label learning with diversity"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "active self paced learning for cost effective and progressive face identification"
    - "multi label learning with global and local label correlation"
    - "self paced multi task learning"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "large scale multi label learning with missing labels"

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.LG(Machine Learning)