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: "empirically analyzing the effect of dataset biases on deep face recognition systems"
  Abstract: It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias have a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities do not share the same pose variation. 4) We demonstrate that our findings on synthetic data also apply when learning from real world data. Our face image generator is publicly available to enable the community to benchmark face recognition systems on a common ground.

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "cross domain image manipulation by demonstration"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "training deep face recognition systems with synthetic data"
    - "neural best buddies sparse cross domain correspondence"
    - "multimodal unsupervised image to image translation"
    - "dida disentangled synthesis for domain adaptation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "xgan unsupervised image to image translation for many to many mappings"

Connected through hyperedge HE-3 (induced by source paper "assessing capsule networks with biased data"):
  with members:
    - "capsule networks for low data transfer learning"
    - "impact of biases in big data"
    - "dynamic routing between capsules"
    - "emnist an extension of mnist to handwritten letters"
    - "evaluating deep convolutional neural networks for material classification"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "an empirical evaluation of current convolutional architectures ability to manage nuisance location and scale variability"

Connected through hyperedge HE-4 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "began boundary equilibrium generative adversarial networks"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-5 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "unrealcv connecting computer vision to unreal engine"

Connected through hyperedge HE-6 (induced by source paper "training deep face recognition systems with synthetic data"):
  with members:
    - "morphable face models an open framework"
    - "do we really need to collect millions of faces for effective face recognition"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "synthetic data for text localisation in natural images"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "frankenstein learning deep face representations using small data"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

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)