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: "attention aware generalized mean pooling for image retrieval"
  Abstract: It has been shown that image descriptors extracted by convolutional neural networks (CNNs) achieve remarkable results for retrieval problems. In this paper, we apply attention mechanism to CNN, which aims at enhancing more relevant features that correspond to important keypoints in the input image. The generated attention-aware features are then aggregated by the previous state-of-the-art generalized mean (GeM) pooling followed by normalization to produce a compact global descriptor, which can be efficiently compared to other image descriptors by the dot product. An extensive comparison of our proposed approach with state-of-the-art methods is performed on the new challenging ROxford5k and RParis6k retrieval benchmarks. Results indicate significant improvement over previous work. In particular, our attention-aware GeM (AGeM) descriptor outperforms state-of-the-art method on ROxford5k under the `Hard' evaluation protocal.

Connected through hyperedge HE-1 (induced by source paper "combination of multiple global descriptors for image retrieval"):
  with members:
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019"
    - "visualizing deep similarity networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "learning discriminative features with multiple granularities for person re identification"
    - "classification is a strong baseline for deep metric learning"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "actnet end to end learning of feature activations and multi stream aggregation for effective instance image retrieval"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "detect to retrieve efficient regional aggregation for image search"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-3 (induced by source paper "actnet end to end learning of feature activations and aggregation for effective instance image retrieval"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "class weighted convolutional features for visual instance search"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-4 (induced by source paper "team jl solution to google landmark recognition 2019"):
  with members:
    - "combination of multiple global descriptors for image retrieval"
    - "understanding and improving kernel local descriptors"
    - "squeeze and excitation networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"

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)