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: "few shot object recognition from machine labeled web images"
  Abstract: With the tremendous advances of Convolutional Neural Networks (ConvNets) on object recognition, we can now obtain reliable enough machine-labeled annotations easily by predictions from off-the-shelf ConvNets. In this work, we present an abstraction memory based framework for few-shot learning, building upon machine-labeled image annotations. Our method takes some large-scale machine-annotated datasets (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots with the form of key-value, where image feature is regarded as key and label embedding serves as value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank, and writes the useful information obtained from related external data into another memory bank, i.e., abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory is correlated to the query example. The abstraction memory concentrates information from the external memory bank, so that it makes the few-shot recognition effective. In the experiments, we firstly confirm that our model can learn to conduct few-shot object recognition on clean human-labeled data from ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources to perform object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great performance, only with a gap of 1% between of the one with human-labeled annotations.

Connected through hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised compositional featureaggregation for few shot recognition"):
  with members:
    - "measuring compositionality in representation learning"
    - "generalizing from a few examples a survey on few shot learning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "learning from web data with memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning from noisy web data with category level supervision"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-6 (induced by source paper "image deformation meta networks for one shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "protogan towards few shot learning for action recognition"):
  with members:
    - "a closer look at few shot classification"
    - "a generative approach to zero shot and few shot action recognition"
    - "multi modal cycle consistent generalized zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"
    - "matching networks for one shot learning"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-8 (induced by source paper "large scale bisample learning on id vs spot face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "additive margin softmax for face verification"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical 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)