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: "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
  Abstract: A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototype propagation network (PPN) trained on few-shot tasks together with data annotated by coarse-label. Given a category graph of the targeted fine-classes and some weakly-labeled coarse-classes, PPN learns an attention mechanism which propagates the prototype of one class to another on the graph, so that the K-nearest neighbor (KNN) classifier defined on the propagated prototypes results in high accuracy across different few-shot tasks. The training tasks are generated by subgraph sampling, and the training objective is obtained by accumulating the level-wise classification loss on the subgraph. The resulting graph of prototypes can be continually re-used and updated for new tasks and classes. We also introduce two practical test/inference settings which differ according to whether the test task can leverage any weakly-supervised information as in training. On two benchmarks, PPN significantly outperforms most recent few-shot learning methods in different settings, even when they are also allowed to train on weakly-labeled data.

Connected through hyperedge HE-1 (induced by source paper "one shot neural architecture search via self evaluated template network"):
  with members:
    - "network pruning via transformable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "densely connected search space for more flexible neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "auto reid searching for a part aware convnet for person re identification"):
  with members:
    - "adaptive exploration for unsupervised person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "searching for a robust neural architecture in four gpu hours"
    - "densely connected search space for more flexible neural architecture search"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "teacher supervises students how to learn from partially labeled images for facial landmark detection"):
  with members:
    - "network pruning via transformable architecture search"
    - "few example object detection with model communication"
    - "quantized densely connected u nets for efficient landmark localization"
    - "autoloss learning discrete schedules for alternate optimization"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "medical concept embedding with time aware attention"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "reinforced mnemonic reader for machine comprehension"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "learning to propagate for graph meta learning"):
  with members:
    - "infinite mixture prototypes for few shot learning"
    - "a closer look at few shot classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"
    - "model agnostic meta learning for fast adaptation of deep 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.LG(Machine Learning)