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 "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  Abstract: Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,866 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is this https URL

Includes members:
  Member "object detection in optical remote sensing images a survey and a new benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning modulated loss for rotated object detection")
  Member "drone based joint density map estimation localization and tracking with space time multi scale attention network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "animal detection in man made environments")
  Member "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles":
    also connected through other hyperedges:
      (none)
  Member "a survey of deep learning based object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection on single monocular images through canonical correlation analysis")
  Member "r 3 net a deep network for multioriented vehicle detection in aerial images and videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rsvqa visual question answering for remote sensing data")
  Member "mrcnet crowd counting and density map estimation in aerial and ground imagery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "animal detection in man made environments")

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)