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 "penet object detection using points estimation in aerial images"):
  Abstract: Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance. However, identifying objects from aerial images faces the following challenges: 1) objects of interests are often too small and too dense relative to the images; 2) objects of interests are often in different relative sizes; and 3) the number of objects in each category is imbalanced. A novel network structure, Points Estimated Network (PENet), is proposed in this work to answer these challenges. PENet uses a Mask Resampling Module (MRM) to augment the imbalanced datasets, a coarse anchor-free detector (CPEN) to effectively predict the center points of the small object clusters, and a fine anchor-free detector FPEN to locate the precise positions of the small objects. An adaptive merge algorithm Non-maximum Merge (NMM) is implemented in CPEN to address the issue of detecting dense small objects, and a hierarchical loss is defined in FPEN to further improve the classification accuracy. Our extensive experiments on aerial datasets visDrone and UAVDT showed that PENet achieved higher precision results than existing state-of-the-art approaches. Our best model achieved 8.7% improvement on visDrone and 20.3% on UAVDT.

Includes members:
  Member "clustered object detection in aerial images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on learning vehicle detection in satellite video")
  Member "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "augmentation for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "residual bi fusion feature pyramid network for accurate single shot object detection")
  Member "vision meets drones a challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "yolo lite a real time object detection algorithm optimized for non gpu computers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "traffic signs detection and recognition system using deep learning")
  Member "detnet a backbone network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")

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)