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 "aerial multi object tracking by detection using deep association networks"):
  Abstract: A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using "Squeeze-and-Excitation" (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.

Includes members:
  Member "ev imo motion segmentation dataset and learning pipeline for event cameras":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large scale event based detection dataset for automotive")
  Member "online multi object tracking with dual matching attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "cascade r cnn high quality object detection and instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascaded human object interaction recognition")
  Member "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust multi modality multi object tracking")
  Member "online multi object tracking with historical appearance matching and scene adaptive detection filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiple object tracking by flowing and fusing")
  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 "deep cosine metric learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan based pose aware regulation for video based person re identification")
  Member "event based moving object detection and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")

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)