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: "multiple instance learning a survey of problem characteristics and applications"
  Abstract: Abstract   Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at  https://github.com/macarbonneau/MILSurvey .

Connected through hyperedge HE-1 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "mba mini batch auc optimization"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "xgboost a scalable tree boosting system"
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "videos as space time region graphs"
    - "step by step erasion one by one collection a weakly supervised temporal action detector"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-3 (induced by source paper "learning to find common objects across few image collections"):
  with members:
    - "attention based deep multiple instance learning"
    - "prototypical networks for few shot learning"
    - "deep self taught learning for weakly supervised object localization"
    - "revisiting knowledge transfer for training object class detectors"
    - "few shot image recognition by predicting parameters from activations"
    - "rapid adaptation with conditionally shifted neurons"
    - "weakly supervised object localization using things and stuff transfer"
    - "searching for activation functions"

Connected through hyperedge HE-4 (induced by source paper "lstms can capture information beyond order"):
  with members:
    - "orderless recurrent models for multi label classification"
    - "multiple instance learning with graph neural networks"
    - "deep spectral clustering using dual autoencoder network"
    - "attention based deep multiple instance learning"
    - "learning and interpreting multi multi instance learning networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual motion gan for future flow embedded video prediction"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health"):
  with members:
    - "attention based deep multiple instance learning"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"

Connected through hyperedge HE-6 (induced by source paper "see and read detecting depression symptoms in higher education students using multimodal social media data"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "exploring the limits of weakly supervised pretraining"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "aggregated residual transformations for deep neural networks"
    - "instagram photos reveal predictive markers of depression"

Connected through hyperedge HE-7 (induced by source paper "dataset2vec learning dataset meta features"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "meta learning with latent embedding optimization"
    - "towards reproducible empirical research in meta learning"
    - "domain generalization via conditional invariant representation"
    - "deep sets"
    - "probabilistic model agnostic meta learning"
    - "warmstarting of model based algorithm configuration"
    - "learning to generalize meta learning for domain generalization"

Connected through hyperedge HE-8 (induced by source paper "ami net a novel multi instance neural network for medical diagnosis from incomplete and imbalanced data"):
  with members:
    - "attention based multi instance neural network for medical diagnosis from incomplete and low quality data"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "attention based deep multiple instance learning"
    - "focal loss for dense object detection"
    - "deep semantic role labeling with self attention"
    - "residual attention network for image classification"
    - "language modeling with gated convolutional networks"
    - "multi instance multi label learning"

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)