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: "recombinator networks learning coarse to fine feature aggregation"
  Abstract: Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision. Max-pooling purposefully discards precise spatial information in order to create features that are more robust, and typically organized as lower resolution spatial feature maps. On some tasks, such as whole-image classification, max-pooling derived features are well suited; however, for tasks requiring precise localization, such as pixel level prediction and segmentation, max-pooling destroys exactly the information required to perform well. Precise localization may be preserved by shallow convnets without pooling but at the expense of robustness. Can we have our max-pooled multi-layered cake and eat it too? Several papers have proposed summation and concatenation based methods for combining upsampled coarse, abstract features with finer features to produce robust pixel level predictions. Here we introduce another model --- dubbed Recombinator Networks --- where coarse features inform finer features early in their formation such that finer features can make use of several layers of computation in deciding how to use coarse features. The model is trained once, end-to-end and performs better than summation-based architectures, reducing the error from the previous state of the art on two facial keypoint datasets, AFW and AFLW, by 30\% and beating the current state-of-the-art on 300W without using extra data. We improve performance even further by adding a denoising prediction model based on a novel convnet formulation.

Connected through hyperedge HE-1 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "spatially transformed adversarial examples"
    - "fast geometrically perturbed adversarial faces"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"

Connected through hyperedge HE-2 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "high resolution representations for labeling pixels and regions"
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-3 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "self supervised learning of a facial attribute embedding from video"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "facial action unit detection using attention and relation learning"
    - "residual parameter transfer for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "panoptic feature pyramid networks"):
  with members:
    - "panoptic segmentation"
    - "group normalization"
    - "learning to fuse things and stuff"
    - "panoptic segmentation with a joint semantic and instance segmentation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "triply supervised decoder networks for joint detection and segmentation"

Connected through hyperedge HE-5 (induced by source paper "imp instance mask projection for high accuracy semantic segmentation of things"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "panoptic feature pyramid networks"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "group normalization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "aggregation via separation boosting facial landmark detector with semi supervised style translation"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-7 (induced by source paper "bridging the domain gap for ground to aerial image matching"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "from third person to first person dataset and baselines for synthesis and retrieval"
    - "cross view image synthesis using conditional gans"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "cross view image synthesis using geometry guided conditional gans"
    - "generative adversarial frontal view to bird view synthesis"
    - "cross view image matching for geo localization in urban environments"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-8 (induced by source paper "mobilefan transferring deep hidden representation for face alignment"):
  with members:
    - "structured knowledge distillation for dense prediction"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "improving fast segmentation with teacher student 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)