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 attribute networks"
  Abstract: Obtaining compact and discriminative features is one of the major challenges in many of the real-world image classification tasks such as face verification and object recognition. One possible approach is to represent input image on the basis of high-level features that carry semantic meaning which humans can understand. In this paper, a model coined deep attribute network (DAN) is proposed to address this issue. For an input image, the model outputs the attributes of the input image without performing any classification. The efficacy of the proposed model is evaluated on unconstrained face verification and real-world object recognition tasks using the LFW and the a-PASCAL datasets. We demonstrate the potential of deep learning for attribute-based classification by showing comparable results with existing state-of-the-art results. Once properly trained, the DAN is fast and does away with calculating low-level features which are maybe unreliable and computationally expensive.

Connected through hyperedge HE-1 (induced by source paper "feature level fusion from facial attributes for face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a deep face identification network enhanced by facial attributes prediction"):
  with members:
    - "multibiometric secure system based on deep learning"
    - "improving facial attribute prediction using semantic segmentation"
    - "face attribute prediction using off the shelf cnn features"
    - "an all in one convolutional neural network for face analysis"
    - "yfcc100m the new data in multimedia research"
    - "a lightened cnn for deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "the new data and new challenges in multimedia research"

Connected through hyperedge HE-3 (induced by source paper "residual codean autoencoder for facial attribute analysis"):
  with members:
    - "adversarial diversity and hard positive generation"
    - "face attribute prediction using off the shelf cnn features"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "affact alignment free facial attribute classification technique"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep learning face attributes in the wild"):
  with members:
    - "object detectors emerge in deep scene cnns"
    - "part based r cnns for fine grained category detection"
    - "self taught object localization with deep networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "aggregate channel features for multi view face detection"
    - "pedestrian detection aided by deep learning semantic tasks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "deeply learned face representations are sparse selective and robust"):
  with members:
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "surpassing human level face verification performance on lfw with gaussianface"
    - "deep learning face representation by joint identification verification"
    - "web scale training for face identification"
    - "panda pose aligned networks for deep attribute modeling"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "panda pose aligned networks for deep attribute modeling"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "pedestrian detection with unsupervised multi stage feature learning"
    - "building high level features using large scale unsupervised learning"

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)