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 cascaded bi network for face hallucination"
  Abstract: We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks, namely face hallucination and dense correspondence field estimation, in a unified framework. In addition, we propose a new gated deep bi-network that contains two functionality-specialized branches to recover different levels of texture details. Extensive experiments demonstrate that such formulation allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations.

Connected through hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "face hallucination with finishing touches"):
  with members:
    - "representation learning by rotating your faces"
    - "multi prototype networks for unconstrained set based face recognition"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "learning to see in the dark"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-3 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning"):
  with members:
    - "facial expression restoration based on improved graph convolutional networks"
    - "fcsr gan joint face completion and super resolution via multi task learning"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "geometry aware face completion and editing"
    - "graph neural networks a review of methods and applications"
    - "rain streak removal for single image via kernel guided cnn"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"

Connected through hyperedge HE-4 (induced by source paper "copy and paste gan face hallucination from shaded thumbnails"):
  with members:
    - "identity preserving face recovery from stylized portraits"
    - "image super resolution using very deep residual channel attention networks"
    - "style aggregated network for facial landmark detection"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "facial attribute capsules for noise face super resolution"):
  with members:
    - "occlusion robust face recognition based on mask learning with pairwisedifferential siamese network"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "mvf net multi view 3d face morphable model regression"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "residual dense network for image super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-6 (induced by source paper "recover and identify a generative dual model for cross resolution person re identification"):
  with members:
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "deep learning for malicious flow detection"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-7 (induced by source paper "fcsr gan joint face completion and super resolution via multi task learning"):
  with members:
    - "feedback network for image super resolution"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "pluralistic image completion"
    - "adaptive transform domain image super resolution via orthogonally regularized deep networks"
    - "geometry aware face completion and editing"
    - "tattoo image search at scale joint detection and compact representation learning"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-8 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"

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)