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 "scaling object detection by transferring classification weights"):
  Abstract: Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number of object-level categories annotated in detection datasets is an order of magnitude smaller than image-level classification labels. State-of-the art object detection models are trained in a supervised fashion and this limits the number of object classes they can detect. In this paper, we propose a novel weight transfer network (WTN) to effectively and efficiently transfer knowledge from classification network's weights to detection network's weights to allow detection of novel classes without box supervision. We first introduce input and feature normalization schemes to curb the under-fitting during training of a vanilla WTN. We then propose autoencoder-WTN (AE-WTN) which uses reconstruction loss to preserve classification network's information over all classes in the target latent space to ensure generalization to novel classes. Compared to vanilla WTN, AE-WTN obtains absolute performance gains of 6% on two Open Images evaluation sets with 500 seen and 57 novel classes respectively, and 25% on a Visual Genome evaluation set with 200 novel classes. The code is available at this https URL.

Includes members:
  Member "scene graph generation with external knowledge and image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object centric image generation from layouts")
  Member "batch normalization is a cause of adversarial vulnerability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "few shot object detection via feature reweighting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incremental few shot object detection")
  Member "pfdet 2nd place solution to open images challenge 2018 object detection track":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "1st place solutions for openimage2019 object detection and instance segmentation")
  Member "exploring the limits of weakly supervised pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "visual and semantic knowledge transfer for large scale semi supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cs r fcn cross supervised learning for large scale object detection")
  Member "zero annotation object detection with web knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse and structured visual attention")
  Member "revisiting knowledge transfer for training object class detectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")

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)