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: "deep learning object detection methods for ecological camera trap data"
  Abstract: Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. Ecological camera traps are a common approach for monitoring an ecosystem's animal population, as they provide continual insight into an environment without being intrusive. However, the analysis of camera trap images is expensive, labour intensive, and time consuming. Recent advances in the field of deep learning for object detection show promise towards automating the analysis of camera trap images. Here, we demonstrate their capabilities by training and comparing two deep learning object detection classifiers, Faster R-CNN and YOLO v2.0, to identify, quantify, and localize animal species within camera trap images using the Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data sets. When trained on large labeled datasets, object recognition methods have shown success. We demonstrate their use, in the context of realistically sized ecological data sets, by testing if object detection methods are applicable for ecological research scenarios when utilizing transfer learning. Faster R-CNN outperformed YOLO v2.0 with average accuracies of 93.0\% and 76.7\% on the two data sets, respectively. Our findings show promising steps towards the automation of the labourious task of labeling camera trap images, which can be used to improve our understanding of the population dynamics of ecosystems across the planet.

Connected through hyperedge HE-1 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "the via annotation software for images audio and video"
    - "large scale interactive object segmentation with human annotators"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "long term temporal context for per camera object detection"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "fcos fully convolutional one stage object detection"
    - "sequence level semantics aggregation for video object detection"
    - "objects as points"
    - "scale mlperf 0 6 models on google tpu v3 pods"
    - "synthetic examples improve generalization for rare classes"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-3 (induced by source paper "a deep active learning system for species identification and counting in camera trap images"):
  with members:
    - "recognition in terra incognita"
    - "in defense of the triplet loss for person re identification"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "similarity learning networks for animal individual re identification beyond the capabilities of a human observer"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "face recognition primates in the wild"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "densely connected convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "conservation ai live stream analysis for the detection of endangered species using convolutional neural networks and drone technology"):
  with members:
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "past present and future approaches using computer vision for animal re identification from camera trap data"):
  with members:
    - "face recognition primates in the wild"
    - "dynamic routing between capsules"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "ssd single shot multibox detector"
    - "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"
    - "automated visual fin identification of individual great white sharks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

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)