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: "attribute centered loss for soft biometrics guided face sketch photo recognition"
  Abstract: Face sketches are able to capture the spatial topology of a face while lacking some facial attributes such as race, skin, or hair color. Existing sketch-photo recognition approaches have mostly ignored the importance of facial attributes. In this paper, we propose a new loss function, called attribute-centered loss, to train a Deep Coupled Convolutional Neural Network (DCCNN) for the facial attribute guided sketch to photo matching. Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes. The DCCNN simultaneously is trained to map photos and pairs of testified attributes and corresponding forensic sketches around their associated centers, while preserving the spatial topology information. Importantly, the centers learn to keep a relative distance from each other, related to their number of contradictory attributes. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D Semi-forensic) databases. The proposed method significantly outperforms the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive overview of biometric fusion"):
  with members:
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"

Connected through hyperedge HE-2 (induced by source paper "deep sketch photo face recognition assisted by facial attributes"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "face attribute prediction using off the shelf cnn features"
    - "sparse graphical representation based discriminant analysis for heterogeneous face recognition"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Connected through hyperedge HE-3 (induced by source paper "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"):
  with members:
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "few shot adversarial domain adaptation"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "the nd iris 0405 iris image dataset"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "generalized bilinear deep convolutional neural networks for multimodal biometric identification"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "the nd iris 0405 iris image dataset"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

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)