human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "seeing without looking contextual rescoring of object detections for ap maximization"):
  Abstract: The majority of current object detectors lack context: class predictions are made independently from other detections. We propose to incorporate context in object detection by post-processing the output of an arbitrary detector to rescore the confidences of its detections. Rescoring is done by conditioning on contextual information from the entire set of detections: their confidences, predicted classes, and positions. We show that AP can be improved by simply reassigning the detection confidence values such that true positives that survive longer (i.e., those with the correct class and large IoU) are scored higher than false positives or detections with small IoU. In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections. The fact that our approach does not require access to visual features makes it computationally inexpensive and agnostic to the detection architecture. In spite of this simplicity, our model consistently improves AP over strong pre-trained baselines (Cascade R-CNN and Faster R-CNN with several backbones), particularly by reducing the confidence of duplicate detections (a learned form of non-maximum suppression) and removing out-of-context objects by conditioning on the confidences, classes, positions, and sizes of the co-occurrent detections (e.g., a high-confidence detection of bird makes a detection of sports ball less likely).

Includes members:
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "structure inference net object detection using scene level context and instance level relationships":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "inner scene similarities as a contextual cue for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual object detection with a few relevant neighbors")
  Member "cascade r cnn delving into high quality object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "proposal learning for semi supervised object detection")
  Member "learning non maximum suppression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "featurenms non maximum suppression by learning feature embeddings")
  Member "spatial memory for context reasoning in object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn")
  Member "exploring the bounds of the utility of context for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t judge an object by its context learning to overcome contextual bias")

Question: Please tell me which class the source paper 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)