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 "looking outside of the box object detection and localization with multi scale patterns"):
  Abstract: Detection and localization of objects at multiple scales often involves sliding a single scale template in order to score windows at different scales independently. Nonetheless, multi-scale visual information at a given image location is highly correlated. This fundamental insight allows us to generalize the traditional multi-scale sliding window technique by jointly considering image features at all scales in order to detect and localize objects. Two max-margin approaches are studied for learning the multi-scale templates and leveraging the highly structured multi-scale information which would have been ignored if a single-scale template was used. The multi-scale formulation is shown to significantly improve general detection performance (measured on the PASCAL VOC dataset). The experimental analysis shows the method to be effective with different visual features, both HOG and CNN. Surprisingly, for a given window in a specific scale, visual information from windows at the same image location but other scales (`out-of-scale' information) contains most of the discriminative information for detection.

Includes members:
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "learning to detect vehicles by clustering appearance patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a layout transfer network for context aware object detection")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "convolutional channel features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fmt fusing multi task convolutional neural network for person search")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "what is holding back convnets for detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synthetic examples improve generalization for rare classes")
  Member "exemplar based deep discriminative and shareable feature learning for scene image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep patch learning for weakly supervised object classification and discovery")
  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)