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 "transductive zero shot learning for 3d point cloud classification"):
  Abstract: Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification. To this end, a novel triplet loss is developed that takes advantage of unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. An extensive set of experiments is carried out, establishing state-of-the-art for ZSL and GZSL in the 3D point cloud domain, as well as demonstrating the applicability of the approach to the image domain.

Includes members:
  Member "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lrc net learning discriminative features on point clouds by encodinglocal region contexts")
  Member "blended convolution and synthesis for efficient discrimination of 3d shapes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "representation learning on unit ball with 3d roto translational equivariance")
  Member "mitigating the hubness problem for zero shot learning of 3d objects":
    also connected through other hyperedges:
      (none)
  Member "representation learning on unit ball with 3d roto translational equivariance":
    also connected through other hyperedges:
      (none)
  Member "zero shot learning of 3d point cloud objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mitigating the hubness problem for zero shot learning of 3d objects")
  Member "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in defense of the triplet loss again learning robust person re identification with fast approximated triplet loss and label distillation")
  Member "deep multiple instance learning for zero shot image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polarity loss for zero shot object detection")
  Member "domain invariant projection learning for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning across meta tasks for few shot learning")

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)