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: "repmet representative based metric learning for classification and one shot object detection"
  Abstract: Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.

Connected through hyperedge HE-1 (induced by source paper "os2d one stage one shot object detection by matching anchor features"):
  with members:
    - "ranked list loss for deep metric learning"
    - "one shot object detection with co attention and co excitation"
    - "one shot instance segmentation"
    - "neighbourhood consensus networks"
    - "lstd a low shot transfer detector for object detection"
    - "end to end representation learning for correlation filter based tracking"
    - "end to end weakly supervised semantic alignment"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "one shot object detection with co attention and co excitation"):
  with members:
    - "comparison network for one shot conditional object detection"
    - "cornernet detecting objects as paired keypoints"
    - "one shot instance segmentation"
    - "few shot object detection via feature reweighting"
    - "lstd a low shot transfer detector for object detection"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "fully convolutional one shot object segmentation for industrial robotics"):
  with members:
    - "fast convergence for object detection by learning how to combine error functions"
    - "one shot segmentation in clutter"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "robotic tactile perception of object properties a review"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep metric learning with angular loss"
    - "interactive perception leveraging action in perception and perception in action"
    - "weakly supervised semantic segmentation based on web image co segmentation"

Connected through hyperedge HE-4 (induced by source paper "comparison detector convolutional neural networks for cervical cell detection"):
  with members:
    - "few example object detection with model communication"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "few shot object detection via feature reweighting"
    - "relational inductive biases deep learning and graph networks"
    - "considerations for a pap smear image analysis system with cnn features"
    - "prototypical networks for few shot learning"
    - "deformable convolutional 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)