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: "automatically identifying wild animals in camera trap images with deep learning"
  Abstract: Having accurate, detailed, and up-to-date information about wildlife location and behavior across broad geographic areas would revolutionize our ability to study, conserve, and manage species and ecosystems. Currently such data are mostly gathered manually at great expense, and thus are sparsely and infrequently collected. Here we investigate the ability to automatically, accurately, and inexpensively collect such data from motion sensor cameras. These camera traps enable pictures of wildlife to be collected inexpensively, unobtrusively, and at high-volume. However, identifying the animals, animal attributes, and behaviors in these pictures remains an expensive, time-consuming, manual task often performed by researchers, hired technicians, or crowdsourced teams of human volunteers. In this paper, we demonstrate that such data can be automatically extracted by deep neural networks (aka deep learning), which is a cutting-edge type of artificial intelligence. In particular, we use the existing human-labeled images from the Snapshot Serengeti dataset to train deep convolutional neural networks for identifying 48 species in 3.2 million images taken from Tanzania's Serengeti National Park. We train neural networks that automatically identify animals with over 92% accuracy. More importantly, we can choose to have our system classify only the images it is highly confident about, allowing valuable human time to be focused only on challenging images. In this case, our automatic animal identification system saves approximately ~8.2 years (at 40 hours per week) of human labeling effort (i.e. over 17,000 hours) while operating on a 3.2-million-image dataset at the same 96.6% accuracy level of crowdsourced teams of human volunteers. Those efficiency gains immediately highlight the importance of using deep neural networks to automate data extraction from camera trap images.

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-2 (induced by source paper "synthetic examples improve generalization for rare classes"):
  with members:
    - "low shot learning from imaginary 3d model"
    - "class balanced loss based on effective number of samples"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "learning category specific mesh reconstruction from image collections"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "recognition in terra incognita"

Connected through hyperedge HE-3 (induced by source paper "efficient method for categorize animals in the wild"):
  with members:
    - "the iwildcam 2018 challenge dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "face recognition primates in the wild"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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"
    - "deep learning object detection methods for ecological camera trap data"
    - "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 "the iwildcam 2019 challenge dataset"):
  with members:
    - "the iwildcam 2018 challenge dataset"
    - "synthetic examples improve generalization for rare classes"
    - "recognition in terra incognita"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-6 (induced by source paper "recognition in terra incognita"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "the devil is in the tails fine grained classification in the wild"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "a deep convolutional neural network for background subtraction"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"

Connected through hyperedge HE-7 (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"
    - "deep learning object detection methods for ecological camera trap data"
    - "dynamic routing between capsules"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deep learning object detection methods for ecological camera trap data"):
  with members:
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "towards automatic wild animal monitoring identification of animal species in camera trap images using very deep convolutional neural networks"
    - "you only look once unified real time object detection"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)