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: "pfdet 2nd place solution to open images challenge 2018 object detection track"
  Abstract: We present a large-scale object detection system by team PFDet. Our system enables training with huge datasets using 512 GPUs, handles sparsely verified classes, and massive class imbalance. Using our method, we achieved 2nd place in the Google AI Open Images Object Detection Track 2018 on Kaggle.

Connected through hyperedge HE-1 (induced by source paper "1st place solutions for openimage2019 object detection and instance segmentation"):
  with members:
    - "revisiting the sibling head in object detector"
    - "detecting 11k classes large scale object detection without fine grained bounding boxes"
    - "double head rcnn rethinking classification and localization for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "cornernet detecting objects as paired keypoints"
    - "gradient harmonized single stage detector"
    - "acquisition of localization confidence for accurate object detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-2 (induced by source paper "scaling object detection by transferring classification weights"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "batch normalization is a cause of adversarial vulnerability"
    - "few shot object detection via feature reweighting"
    - "exploring the limits of weakly supervised pretraining"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "zero annotation object detection with web knowledge transfer"
    - "revisiting knowledge transfer for training object class detectors"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (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"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "yolov3 an incremental improvement"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-4 (induced by source paper "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"):
  with members:
    - "delta gpu performance model for deep learning applications with in depth memory system traffic analysis"
    - "supporting very large models using automatic dataflow graph partitioning"
    - "high accuracy low precision training"
    - "dynamic sparse graph for efficient deep learning"
    - "measuring the effects of data parallelism on neural network training"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "megdet a large mini batch object detector"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (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"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning effective visual relationship detector on 1 gpu"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "deep high resolution representation learning for visual recognition"
    - "deep high resolution representation learning for human pose estimation"
    - "scene graph prediction with limited labels"
    - "graph r cnn for scene graph generation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-7 (induced by source paper "optuna a next generation hyperparameter optimization framework"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "tensorflow a system for large scale machine learning"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-8 (induced by source paper "nocaps novel object captioning at scale"):
  with members:
    - "evalai towards better evaluation systems for ai agents"
    - "neural baby talk"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "decoupled novel object captioner"
    - "partially supervised image captioning"

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)