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: "dafe fd density aware feature enrichment for face detection"
  Abstract: Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module. This module, inspired by recent crowd counting/density estimation techniques, performs the task of estimating the per pixel density of people/faces present in the image. Output of this module is employed to accentuate the feature maps from the backbone network using a feature enrichment module before being used for detecting smaller faces. The proposed approach can be used to complement recent anchor-design based novel methods to further improve their results. Experiments conducted on different datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "bam bottleneck attention module"

Connected through hyperedge HE-2 (induced by source paper "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-3 (induced by source paper "locate size and count accurately resolving people in dense crowds via detection"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "point in box out beyond counting persons in crowds"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "inverse attention guided deep crowd counting network"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "reverse attention for salient object detection"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-5 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering 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.CV(Computer Vision and Pattern Recognition)