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: "virtual class enhanced discriminative embedding learning"
  Abstract: Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged. In this paper, we propose a novel yet extremely simple method \textbf{Virtual Softmax} to enhance the discriminative property of learned features by injecting a dynamic virtual negative class into the original softmax. Injecting virtual class aims to enlarge inter-class margin and compress intra-class distribution by strengthening the decision boundary constraint. Although it seems weird to optimize with this additional virtual class, we show that our method derives from an intuitive and clear motivation, and it indeed encourages the features to be more compact and separable. This paper empirically and experimentally demonstrates the superiority of Virtual Softmax, improving the performances on a variety of object classification and face verification tasks.

Connected through hyperedge HE-1 (induced by source paper "mixed high order attention network for person re identification"):
  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"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "discriminative learning of latent features for zero shot recognition"

Connected through hyperedge HE-2 (induced by source paper "attract or distract exploit the margin of open set"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "open set domain adaptation by backpropagation"
    - "learning factorized representations for open set domain adaptation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "contrastive center loss for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "mis classified vector guided softmax loss for face recognition"):
  with members:
    - "a high efficiency framework for constructing large scale face parsing benchmark"
    - "the devil of face recognition is in the noise"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-4 (induced by source paper "on the separability of classes with the cross entropy loss function"):
  with members:
    - "g softmax improving intra class compactness and inter class separability of features"
    - "entropy and mutual information in models of deep neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for large scale image recognition"

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)