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: "sampling techniques for large scale object detection from sparsely annotated objects"
  Abstract: Efficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector. In order to take the incompleteness of these datasets into account, one possibility is to use pretrained models to detect the presence of the unverified objects. However, the performance of such a strategy depends largely on the power of the pretrained model. In this study, we propose part-aware sampling, a method that uses human intuition for the hierarchical relation between objects. In terse terms, our method works by making assumptions like "a bounding box for a car should contain a bounding box for a tire". We demonstrate the power of our method on OID and compare the performance against a method based on a pretrained model. Our method also won the first and second place on the public and private test sets of the Google AI Open Images Competition 2018.

Connected through hyperedge HE-1 (induced by source paper "solving missing annotation object detection with background recalibration loss"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "multimodal unsupervised image to image translation"
    - "gradient harmonized single stage detector"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft sampling for robust object detection"
    - "focal loss for dense object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "learning an efficient network for large scale hierarchical object detection with data imbalance 3rd place solution to open images challenge 2019"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "large scale interactive object segmentation with human annotators"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"
    - "rethinking imagenet pre training"
    - "yolov3 an incremental improvement"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "team pfdet s methods for open images challenge 2019"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "learning data augmentation strategies for object detection"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "squeeze and excitation networks"
    - "megdet a large mini batch object detector"
    - "chainercv a library for deep learning in computer vision"

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)