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: "contrastive center loss for deep neural networks"
  Abstract: The deep convolutional neural network(CNN) has significantly raised the performance of image classification and face recognition. Softmax is usually used as supervision, but it only penalizes the classification loss. In this paper, we propose a novel auxiliary supervision signal called contrastivecenter loss, which can further enhance the discriminative power of the features, for it learns a class center for each class. The proposed contrastive-center loss simultaneously considers intra-class compactness and inter-class separability, by penalizing the contrastive values between: (1)the distances of training samples to their corresponding class centers, and (2)the sum of the distances of training samples to their non-corresponding class centers. Experiments on different datasets demonstrate the effectiveness of contrastive-center loss.

Connected through hyperedge HE-1 (induced by source paper "multiple discrimination and pairwise cnn for view based 3d object retrieval"):
  with members:
    - "hypergraph neural networks"
    - "gift a real time and scalable 3d shape search engine"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a useful taxonomy for adversarial robustness of neural networks"):
  with members:
    - "adversarial defense by suppressing high frequency components"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "metric learning for adversarial robustness"
    - "adversarial robustness may be at odds with simplicity"
    - "are labels required for improving adversarial robustness"
    - "batch normalization is a cause of adversarial vulnerability"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "adversarial attacks and defenses in images graphs and text a review"

Connected through hyperedge HE-3 (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"
    - "virtual class enhanced discriminative embedding learning"

Connected through hyperedge HE-4 (induced by source paper "accurate and efficient similarity search for large scale face recognition"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "billion scale similarity search with gpus"
    - "aggregated residual transformations for deep neural networks"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "deep image retrieval learning global representations for image search"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "attribute centered loss for soft biometrics guided face sketch photo recognition"):
  with members:
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "face sketch matching via coupled deep transform learning"
    - "face attribute prediction using off the shelf cnn features"
    - "sparse graphical representation based discriminant analysis for heterogeneous face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "defrag deep euclidean feature representations through adaptation on the grassmann manifold"):
  with members:
    - "densely connected convolutional networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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)