human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "descriptor learning for omnidirectional image matching"
  Abstract: Feature matching in omnidirectional vision systems is a challenging problem, mainly because complicated optical systems make the theoretical modelling of invariance and construction of invariant feature descriptors hard or even impossible. In this paper, we propose learning invariant descriptors using a training set of similar and dissimilar descriptor pairs. We use the similarity-preserving hashing framework, in which we are trying to map the descriptor data to the Hamming space preserving the descriptor similarity on the training set. A neural network is used to solve the underlying optimization problem. Our approach outperforms not only straightforward descriptor matching, but also state-of-the-art similarity-preserving hashing methods.

Connected through hyperedge HE-1 (induced by source paper "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"):
  with members:
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "normface l 2 hypersphere embedding for face verification"
    - "prototypical networks for few shot learning"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"
    - "deep metric learning via facility location"

Connected through hyperedge HE-2 (induced by source paper "on learning density aware embeddings"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "deep metric learning via facility location"
    - "hard aware deeply cascaded embedding"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "smart mining for deep metric learning"):
  with members:
    - "no fuss distance metric learning using proxies"
    - "fast training of triplet based deep binary embedding networks"
    - "training region based object detectors with online hard example mining"
    - "deep metric learning via facility location"
    - "learning deep embeddings with histogram loss"
    - "hard aware deeply cascaded embedding"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-4 (induced by source paper "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"):
  with members:
    - "learning descriptors for object recognition and 3d pose estimation"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional neural networks"
    - "deep metric learning using triplet network"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-5 (induced by source paper "sparse similarity preserving hashing"):
  with members:
    - "learning efficient sparse and low rank models"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-6 (induced by source paper "multimodal similarity preserving hashing"):
  with members:
    - "learning multi modal similarity"
    - "positive semidefinite metric learning with boosting"

Question: Please tell me which class the center node 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)