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 "furnishing your room by what you see an end to end furniture set retrieval framework with rich annotated benchmark dataset"):
  Abstract: Understanding interior scenes has attracted enormous interest in computer vision community. However, few works focus on the understanding of furniture within the scenes and a large-scale dataset is also lacked to advance the field. In this paper, we first fill the gap by presenting DeepFurniture, a richly annotated large indoor scene dataset, including 24k indoor images, 170k furniture instances and 20k unique furniture identities. On the dataset, we introduce a new benchmark, named furniture set retrieval. Given an indoor photo as input, the task requires to detect all the furniture instances and search a matched set of furniture identities. To address this challenging task, we propose a feature and context embedding based framework. It contains 3 major contributions: (1) An improved Mask-RCNN model with an additional mask-based classifier is introduced for better utilizing the mask information to relieve the occlusion problems in furniture detection context. (2) A multi-task style Siamese network is proposed to train the feature embedding model for retrieval, which is composed of a classification subnet supervised by self-clustered pseudo attributes and a verification subnet to estimate whether the input pair is matched. (3) In order to model the relationship of the furniture entities in an interior design, a context embedding model is employed to re-rank the retrieval results. Extensive experiments demonstrate the effectiveness of each module and the overall system.

Includes members:
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "interiornet mega scale multi sensor photo realistic indoor scenes dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "unified perceptual parsing for scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "in defense of grid features for visual question answering")
  Member "deformable convnets v2 more deformable better results":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "in defense of grid features for visual question answering")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "soft nms improving object detection with one line of code":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting the sibling head in object detector")

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)