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: "end to end people detection in crowded scenes"
  Abstract: Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes.

Connected through hyperedge HE-1 (induced by source paper "set structured latent representations"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "fspool learning set representations with featurewise sort pooling"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "deep set prediction networks"
    - "multi object representation learning with iterative variational inference"
    - "videograph recognizing minutes long human activities in videos"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "object based reasoning in vqa"

Connected through hyperedge HE-2 (induced by source paper "a video analysis method on wanfang dataset via deep neural network"):
  with members:
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "microsoft coco common objects in context"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-4 (induced by source paper "mdfn multi scale deep feature learning network for object detection"):
  with members:
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "a 2 nets double attention networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive nms refining pedestrian detection in a crowd"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "acquisition of localization confidence for accurate object detection"
    - "iterative crowd counting"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "soft nms improving object detection with one line of code"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (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"
    - "dafe fd density aware feature enrichment for face detection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "point in box out beyond counting persons in crowds"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "zigzag learning for weakly supervised object detection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "revisiting perspective information for efficient crowd counting"
    - "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-8 (induced by source paper "relational learning for joint head and human detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "to boost or not to boost on the limits of boosted trees for object detection"

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)