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: "presentation attack detection for iris recognition an assessment of the state of the art"
  Abstract: Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future, and a short list of recommended readings are suggested.

Connected through hyperedge HE-1 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "convolutional neural networks for automatic meter reading"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "demographic bias in presentation attack detection of iris recognition systems"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "robust iris presentation attack detection fusing 2d and 3d information"):
  with members:
    - "data driven segmentation of post mortem iris images"
    - "open source presentation attack detection baseline for iris recognition"
    - "iris presentation attack detection based on photometric stereo features"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples to fool iris recognition systems"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fast geometrically perturbed adversarial faces"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial diversity and hard positive generation"
    - "stealing machine learning models via prediction apis"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "ensemble of multi view learning classifiers for cross domain iris presentation attack detection"):
  with members:
    - "human iris recognition in post mortem subjects study and database"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-6 (induced by source paper "presentation attack detection for cadaver iris"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data driven segmentation of post mortem iris images"
    - "iris recognition after death"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "human iris recognition in post mortem subjects study and database"
    - "exploring models and data for image question answering"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (induced by source paper "open source presentation attack detection baseline for iris recognition"):
  with members:
    - "iris presentation attack detection based on photometric stereo features"
    - "human iris recognition in post mortem subjects study and database"

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)