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 "domain size pooling in local descriptors dsp sift"):
  Abstract: We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.

Includes members:
  Member "dense correspondences across scenes and scales":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dense scale selection over space time and space time")
  Member "visual scene representations contrast scaling and occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for passive synthetic aperture radar")
  Member "return of the devil in the details delving deep into convolutional nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep hashing methods")
  Member "multi scale orderless pooling of deep convolutional activation features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "descriptor matching with convolutional neural networks a comparison to sift":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning transformation aware embeddings for image forensics")
  Member "on the design and analysis of multiple view descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual scene representations contrast scaling and occlusion")
  Member "unsupervised feature learning by augmenting single images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmentor an image augmentation library for machine learning")
  Member "scene parsing with multiscale feature learning purity trees and optimal covers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks")

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)