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 "centroid based scene classification cbsc using deep features and clustering for rgb d indoor scene classification"):
  Abstract: This paper contributes a novel method for RGB-D indoor scene classification. Recent approaches to this problem focus on developing increasingly complex pipelines that learn correlated features across the RGB and depth modalities. In contrast, this paper presents a simple method that first extracts features for the RGB and depth modalities using Places365-CNN and fine-tuned Places365-CNN on depth data, respectively and then clusters these features to generate a set of centroids representing each scene category from the training data. For classification a scene image is converted to CNN features and the distance of these features to the n closest learned centroids is used to predict the image's category. We evaluate our method on two standard RGB-D indoor scene classification benchmarks: SUNRGB-D and NYU Depth V2 and demonstrate that our proposed classification approach achieves superior performance over the state-of-the-art methods on both datasets.

Includes members:
  Member "learning effective rgb d representations for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "indoor scene recognition in 3d")
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "weakly supervised patchnets describing and aggregating local patches for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "joint unsupervised learning of deep representations and image clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "netvlad cnn architecture for weakly supervised place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "understand scene categories by objects a semantic regularized scene classifier using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi task learning with coarse priors for robust part aware person re identification")
  Member "exploiting local features from deep networks for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)