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: "almn deep embedding learning with geometrical virtual point generating"
  Abstract: Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive. To this end, we propose Adaptive Large Margin N-Pair loss (ALMN) to address the aforementioned issues. Instead of exploring hard example-mining strategy, we introduce the concept of large margin constraint. This constraint aims at encouraging local-adaptive large angular decision margin among dissimilar samples in multimodal feature space so as to significantly encourage intraclass compactness and interclass separability. And it is mainly achieved by a simple yet novel geometrical Virtual Point Generating (VPG) method, which converts artificially setting a fixed margin into automatically generating a boundary training sample in feature space and is an open question. We demonstrate the effectiveness of our method on several popular datasets for image retrieval and clustering tasks.

Connected through hyperedge HE-1 (induced by source paper "hybrid attention based decoupled metric learning for zero shot image retrieval"):
  with members:
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "zero shot sketch image hashing"
    - "discriminative learning of latent features for zero shot recognition"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-2 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "zero shot sketch image hashing"
    - "sampling matters in deep embedding learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"
    - "disturblabel regularizing cnn on the loss layer"

Connected through hyperedge HE-3 (induced by source paper "signal to noise ratio a robust distance metric for deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "deep metric learning with angular loss"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "deep supervised hashing with triplet labels"
    - "training region based object detectors with online hard example mining"

Connected through hyperedge HE-4 (induced by source paper "virtual class enhanced discriminative embedding learning"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "active convolution learning the shape of convolution for image classification"
    - "squeeze and excitation networks"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "disturblabel regularizing cnn on the loss layer"
    - "densely connected convolutional networks"

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)