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: "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
  Abstract: This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors, such as variations in illumination, object appearance and scale, partial occlusions, and cluttered backgrounds—particularly important when a search is performed across very large datasets with significant variability. We propose a novel CNN-based global descriptor, called REMAP, which learns and aggregates a hierarchy of deep features from multiple CNN layers, and is trained end-to-end with a triplet loss. REMAP explicitly learns discriminative features which are mutually supportive and complementary at various semantic levels of visual abstraction. These dense local features are max-pooled spatially at each layer, within multi-scale overlapping regions, before aggregation into a single image-level descriptor. To identify the semantically useful regions and layers for retrieval, we propose to measure the information gain of each region and layer using KL-divergence. Our system effectively learns during training how useful various regions and layers are and weights them accordingly. We show that such relative entropy-guided aggregation outperforms classical CNN-based aggregation controlled by SGD. The entire framework is trained in an end-to-end fashion, outperforming the latest state-of-the-art results. On image retrieval datasets Holidays, Oxford, and MPEG, the REMAP descriptor achieves mAP of 95.5%, 91.5%, and 80.1%, respectively, outperforming any results published to date. REMAP also formed the core of the winning submission to the Google Landmark Retrieval Challenge on Kaggle.

Connected through hyperedge HE-1 (induced by source paper "deep active learning for biased datasets via fisher kernel self supervision"):
  with members:
    - "variational adversarial active learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "interpreting black box predictions using fisher kernels"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to reweight examples for robust deep learning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "emergence of invariance and disentanglement in deep representations"

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:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "detect to retrieve efficient regional aggregation for image search"
    - "attention aware generalized mean pooling for image retrieval"
    - "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:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "attention aware generalized mean pooling for image retrieval"
    - "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"

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)