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: "git loss for deep face recognition"
  Abstract: Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks, such as face recognition and verification, and have achieved state-of-the-art results due to their ability to capture discriminative deep features. Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss. In order to further enhance the discriminative capability of deep features, we introduce a joint supervision signal, Git loss, which leverages on softmax and center loss functions. The aim of our loss function is to minimize the intra-class variations as well as maximize the inter-class distances. Such minimization and maximization of deep features are considered ideal for face recognition task. We perform experiments on two popular face recognition benchmarks datasets and show that our proposed loss function achieves maximum separability between deep face features of different identities and achieves state-of-the-art accuracy on two major face recognition benchmark datasets: Labeled Faces in the Wild (LFW) and YouTube Faces (YTF). However, it should be noted that the major objective of Git loss is to achieve maximum separability between deep features of divergent identities.

Connected through hyperedge HE-1 (induced by source paper "few shot learning for domain specfic fine grained image classfication"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few shot learning with metric agnostic conditional embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "multi scale location aware kernel representation for object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "dynamic few shot visual learning without forgetting"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi level similarity learning for low shot recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few shot learning with metric agnostic conditional embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "low shot learning from imaginary data"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "meta sgd learning to learn quickly for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "audio visual event localization in unconstrained videos"
    - "learnable pins cross modal embeddings for person identity"
    - "voxceleb2 deep speaker recognition"
    - "on learning associations of faces and voices"
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-4 (induced by source paper "learning inward scaled hypersphere embedding exploring projections in higher dimensions"):
  with members:
    - "revisiting cross modal retrieval"
    - "random erasing data augmentation"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "learning deep structure preserving image text embeddings"

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)