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: "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
  Abstract: In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple feature abstraction levels. Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification. Features extracted at different convolutional layers of a modality-specific CNN represent the input at several different levels of abstract representations. We demonstrate that an efficient multimodal classification can be accomplished with a significant reduction in the number of network parameters by exploiting these multi-level abstract representations extracted from all the modality-specific CNNs. We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features from the last layer of each modality-specific CNNs. We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy. We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.

Connected through hyperedge HE-1 (induced by source paper "defending against adversarial iris examples using wavelet decomposition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "fast geometrically perturbed adversarial faces"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-2 (induced by source paper "adversarial examples to fool iris recognition systems"):
  with members:
    - "fast geometrically perturbed adversarial faces"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "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-3 (induced by source paper "learning to authenticate with deep multibiometric hashing and neural network decoding"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "deep learning methods for improved decoding of linear codes"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "rnn decoding of linear block codes"
    - "neural offset min sum decoding"
    - "learning to decode linear codes using deep learning"
    - "the nd iris 0405 iris image dataset"

Connected through hyperedge HE-4 (induced by source paper "periocular recognition in the wild with orthogonal combination of local binary coded pattern in dual stream convolutional neural network"):
  with members:
    - "binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification"
    - "convolutional two stream network fusion for video action recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-5 (induced by source paper "id preserving generative adversarial network for partial latent fingerprint reconstruction"):
  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"
    - "generative convolutional networks for latent fingerprint reconstruction"
    - "perceptual losses for real time style transfer and super resolution"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deep sketch photo face recognition assisted by facial attributes"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "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-7 (induced by source paper "generalized bilinear deep convolutional neural networks for multimodal biometric identification"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "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.LG(Machine Learning)