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: "longitudinal study of child face recognition"
  Abstract: We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.

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 "child face age progression via deep feature aging"):
  with members:
    - "decorrelated adversarial learning for age invariant face recognition"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "face aging with conditional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "finding missing children aging deep face features"):
  with members:
    - "how are attributes expressed in face dcnns"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "identity document to selfie face matching across adolescence"):
  with members:
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "docface id document to selfie matching"
    - "cross domain deep face matching for real banking security systems"
    - "large scale bisample learning on id vs spot face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "ms celeb 1m a dataset and benchmark for large scale face 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)