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: "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
  Abstract: With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches.

Connected through hyperedge HE-1 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "defending against adversarial iris examples using wavelet decomposition"):
  with members:
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "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"
    - "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-3 (induced by source paper "attribute guided coupled gan for cross resolution face recognition"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "style and content disentanglement in generative adversarial networks"
    - "synthesis based robust low resolution face recognition"

Connected through hyperedge HE-4 (induced by source paper "identity aware deep face hallucination via adversarial face verification"):
  with members:
    - "sigan siamese generative adversarial network for identity preserving face hallucination"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "style and content disentanglement in generative adversarial networks"
    - "multibiometric secure system based on deep learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "biometrics as a service a framework to promote innovative biometric recognition in the cloud"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"

Connected through hyperedge HE-5 (induced by source paper "zero shot deep hashing and neural network based error correction for face template protection"):
  with members:
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "deep learning methods for improved decoding of linear codes"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-6 (induced by source paper "feature level fusion from facial attributes for face recognition"):
  with members:
    - "attribute guided coupled gan for cross resolution face recognition"
    - "label propagation for deep semi supervised learning"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "face attribute prediction using off the shelf cnn features"
    - "restoring highly corrupted images by impulse noise using radial basis functions interpolation"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "learning to authenticate with deep multibiometric hashing and neural network decoding"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "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"

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)