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 "baod budget aware object detection"):
  Abstract: We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly supervised) to annotate them with. We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation. We conduct a comprehensive empirical study showing that a handcrafted optimization method outperforms other selection techniques including random sampling, uncertainty sampling and active learning. By combining an optimal image/annotation selection scheme with hybrid supervised learning to solve the BAOD problem, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12.8% of its original annotation budget. Furthermore, when $100\%$ of the budget is used, it surpasses this performance by 2.0 mAP percentage points.

Includes members:
  Member "a flexible model for training action localization with varying levels of supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "pcn part and context information for pedestrian detection with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hybrid channel based pedestrian detection")
  Member "crowd counting by adaptively fusing predictions from an image pyramid":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "localization aware active learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active adversarial domain adaptation")
  Member "towards practical visual search engine within elasticsearch":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical comparison of faiss and fenshses for nearest neighbor search in hamming space")
  Member "generative adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "active adversarial domain adaptation")
  Member "learning active learning from data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action for better prediction")
  Member "extreme clicking for efficient object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

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)