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 representations for cross spectral ocular biometrics"
  Abstract: One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross-spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non-normalised iris–periocular regions, the authors fine-tune convolutional neural network models, originally trained for face recognition. On the basis of the experiments carried out in two publicly available cross-spectral ocular databases, they report results for intra-spectral and cross-spectral scenarios, with the best performance being observed when fusing ResNet-50 deep representations from both the periocular and iris regions. When compared to the state of the art, they observed that the proposed solution consistently reduces the equal error ratevalues by 90%/93%/96% and 61%/77%/83% on the cross-spectral scenario and in the PolyU bi-spectral and cross-eye–cross-spectral datasets. Finally, they evaluate the effect that the ‘deepness’ factor of feature representations has in recognition effectiveness, and based on a subjective analysis of the most problematic pairwise comparisons – they point out further directions for this field of research.

Connected through hyperedge HE-1 (induced by source paper "spectrum translation for cross spectral ocular matching"):
  with members:
    - "matching thermal to visible face images using a semantic guided generative adversarial network"
    - "a survey on periocular biometrics research"
    - "cross spectral periocular recognition a survey"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "unconstrained periocular recognition using generative deep learning frameworks for attribute normalization"):
  with members:
    - "ocular recognition databases and competitions a survey"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "a style based generator architecture for generative adversarial networks"
    - "attgan facial attribute editing by only changing what you want"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "fader networks manipulating images by sliding attributes"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-3 (induced by source paper "ocular recognition databases and competitions a survey"):
  with members:
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "openeds open eye dataset"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"

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)