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: "deep convolutional neural network features and the original image"
  Abstract: Face recognition algorithms based on deep convolutional neural networks (DCNNs) have made progress on the task of recognizing faces in unconstrained viewing conditions. These networks operate with compact feature-based face representations derived from learning a very large number of face images. While the learned features produced by DCNNs can be highly robust to changes in viewpoint, illumination, and appearance, little is known about the nature of the face code that emerges at the top level of such networks. We analyzed the DCNN features produced by two face recognition algorithms. In the first set of experiments we used the top-level features from the DCNNs as input into linear classifiers aimed at predicting metadata about the images. The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame. In the second set of experiments, we measured the extent to which individual DCNN features operated in a view-dependent or view-invariant manner. We found that view-dependent coding was a characteristic of the identities rather than the DCNN features - with some identities coded consistently in a view-dependent way and others in a view-independent way. In our third analysis, we visualized the DCNN feature space for over 24,000 images of 500 identities. Images in the center of the space were uniformly of low quality (e.g., extreme views, face occlusion, low resolution). Image quality increased monotonically as a function of distance from the origin. This result suggests that image quality information is available in the DCNN features, such that consistently average feature values reflect coding failures that reliably indicate poor or unusable images. Combined, the results offer insight into the coding mechanisms that support robust representation of faces in DCNNs.

Connected through hyperedge HE-1 (induced by source paper "arcface additive angular margin loss for deep face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "rethinking feature distribution for loss functions in image classification"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"
    - "face recognition via centralized coordinate learning"

Connected through hyperedge HE-2 (induced by source paper "crystal loss and quality pooling for unconstrained face verification and recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "the do s and don ts for cnn based face verification"
    - "a proximity aware hierarchical clustering of faces"
    - "faceposenet making a case for landmark free face alignment"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"

Connected through hyperedge HE-3 (induced by source paper "l2 constrained softmax loss for discriminative face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "a proximity aware hierarchical clustering of faces"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "aggregated residual transformations for deep neural 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)