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 "a zero shot based fingerprint presentation attack detection system"):
  Abstract: With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar.

Includes members:
  Member "rethinking zero shot learning a conditional visual classification perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning feature to feature translator by alternating back propagation for generative zero shot learning")
  Member "oct fingerprints resilience to presentation attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fingerprint spoof generalization")
  Member "multi modal cycle consistent generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "a zero shot framework for sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "transductive unbiased embedding for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "transductive zero shot learning with adaptive structural embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transfer adaptation learning a decade survey")
  Member "deepmasterprints generating masterprints for dictionary attacks via latent variable evolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "synthesized classifiers for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")

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)