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: "the freiburg groceries dataset"
  Abstract: With the increasing performance of machine learning techniques in the last few years, the computer vision and robotics communities have created a large number of datasets for benchmarking object recognition tasks. These datasets cover a large spectrum of natural images and object categories, making them not only useful as a testbed for comparing machine learning approaches, but also a great resource for bootstrapping different domain-specific perception and robotic systems. One such domain is domestic environments, where an autonomous robot has to recognize a large variety of everyday objects such as groceries. This is a challenging task due to the large variety of objects and products, and where there is great need for real-world training data that goes beyond product images available online. In this paper, we address this issue and present a dataset consisting of 5,000 images covering 25 different classes of groceries, with at least 97 images per class. We collected all images from real-world settings at different stores and apartments. In contrast to existing groceries datasets, our dataset includes a large variety of perspectives, lighting conditions, and degrees of clutter. Overall, our images contain thousands of different object instances. It is our hope that machine learning and robotics researchers find this dataset of use for training, testing, and bootstrapping their approaches. As a baseline classifier to facilitate comparison, we re-trained the CaffeNet architecture (an adaptation of the well-known AlexNet) on our dataset and achieved a mean accuracy of 78.9%. We release this trained model along with the code and data splits we used in our experiments.

Connected through hyperedge HE-1 (induced by source paper "rethinking object detection in retail stores"):
  with members:
    - "reppoints point set representation for object detection"
    - "data priming network for automatic check out"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "rpc a large scale retail product checkout dataset"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "mvtec d2s densely segmented supermarket dataset"

Connected through hyperedge HE-2 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "on the convergence of adam and beyond"
    - "cinic 10 is not imagenet or cifar 10"

Connected through hyperedge HE-3 (induced by source paper "product image recognition with guidance learning and noisy supervision"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deep learning is robust to massive label noise"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-4 (induced by source paper "data priming network for automatic check out"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deeply supervised salient object detection with short connections"
    - "mvtec d2s densely segmented supermarket dataset"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "detecting retail products in situ using cnn without human effort labeling"):
  with members:
    - "fssd feature fusion single shot multibox detector"
    - "dssd deconvolutional single shot detector"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "deep learning for single view instance recognition"

Connected through hyperedge HE-6 (induced by source paper "quantifying the effect of representations on task complexity"):
  with members:
    - "where is the information in a deep neural network"
    - "weight agnostic neural networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep residual learning for image recognition"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mvtec d2s densely segmented supermarket dataset"):
  with members:
    - "focal loss for dense object detection"
    - "oriented response networks"
    - "perceptual generative adversarial networks for small object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic understanding of scenes through the ade20k dataset"
    - "stacked generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-8 (induced by source paper "faster bounding box annotation for object detection in indoor scenes"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "focal loss for dense object detection"
    - "extreme clicking for efficient object annotation"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "faster r cnn towards real time object detection with region proposal 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)