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 "prototypicality effects in global semantic description of objects"):
  Abstract: We propose a new approach to face the semantic features descriptions of objects based in the prototypicality effects of prototypes theory. Our descriptor, called global semantic descriptor, is capable of coding and storing a semantic (central and peripheral) meaning of object. Our model compute the semantic prototype of object using features extracted by CNN-classifications models. We propose a simple method to reduce the dimensionality of semantic prototype without semantic information lost. We demonstrated that our descriptor preserved de semantic information used by the CNN-models for classifications task. The experiments on MNIST and ImageNet Datasets demonstrated that our model allows clustering the elements into family resemblance (typicality value) within the category.

Includes members:
  Member "end to end weakly supervised semantic alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fcss fully convolutional self similarity for dense semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  Member "scnet learning semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  Member "lift learned invariant feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)