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: "feature fused ssd fast detection for small objects"
  Abstract: Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.

Connected through hyperedge HE-1 (induced by source paper "dc spp yolo dense connection and spatial pyramid pooling based yolo for object detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "extend the shallow part of single shot multibox detector via convolutional neural network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "evolving boxes for fast vehicle detection"
    - "r fcn 3000 at 30fps decoupling detection and classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "augmentation for small object detection"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "dssd deconvolutional single shot detector"
    - "perceptual generative adversarial networks for small object detection"
    - "improving small object proposals for company logo detection"
    - "the devil is in the decoder classification regression and gans"
    - "semantic instance segmentation via deep metric learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "small target detection for search and rescue operations using distributed deep learning and synthetic data generation"):
  with members:
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks"
    - "automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model"
    - "deep neural networks for pattern recognition"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "detecting small signs from large images"
    - "unrealcv connecting computer vision to unreal engine"
    - "mocap guided data augmentation for 3d pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "fire ssd wide fire modules based single shot detector on edge device"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"):
  with members:
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an edge fog cloud platform for anticipatory learning process designed for internet of mobile things"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "towards lightweight convolutional neural networks for object detection"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "liver lesion detection from weakly labeled multi phase ct volumes with a grouped single shot multibox detector"):
  with members:
    - "fssd feature fusion single shot multibox detector"
    - "context aware single shot detector"
    - "liver lesion segmentation informed by joint liver segmentation"
    - "automatic liver lesion detection using cascaded deep residual networks"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "densely connected convolutional networks"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-7 (induced by source paper "ensemble based adaptive single shot multi box detector"):
  with members:
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "parsenet looking wider to see better"

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)