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 "power normalizing second order similarity network for few shot learning"):
  Abstract: Second- and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained image and scene recognition. However, these statistics need to be passed via an appropriate pooling scheme to obtain the best performance. Power Normalizations are non-linear activation units which enjoy probability-inspired derivations and can be applied in CNNs. In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations. To this end, we propose several formulations capturing second-order statistics and derive a sigmoid-like Power Normalizing function to demonstrate its interpretability. Our model is trained end-to-end to learn the similarity between the support set and query images for the problem of one- and few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC datasets demonstrate that this network obtains state-of-the-art results on several few-shot learning protocols.

Includes members:
  Member "zero shot kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")
  Member "a deeper look at power normalizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "model selection for generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot learning via saliency guided hallucination of samples")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "feature generating networks for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "domain adaptation by mixture of alignments of second or higher order scatter tensors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation")
  Member "tensor representations via kernel linearization for action recognition from 3d skeletons":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")

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)