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: "surveillance face recognition challenge"
  Abstract: Face recognition (FR) is one of the most extensively investigated problems in computer vision. Significant progress in FR has been made due to the recent introduction of the larger scale FR challenges, particularly with constrained social media web images, e.g. high-resolution photos of celebrity faces taken by professional photo-journalists. However, the more challenging FR in unconstrained and low-resolution surveillance images remains largely under-studied. To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly the only true surveillance FR benchmark to our best knowledge, where low-resolution images are not synthesised by artificial down-sampling of native high-resolution images. This challenge contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. As a consequence, it presents an extremely challenging FR benchmark. We benchmark the FR performance on this challenge using five representative deep learning face recognition models, in comparison to existing benchmarks. We show that the current state of the arts are still far from being satisfactory to tackle the under-investigated surveillance FR problem in practical forensic scenarios. Face recognition is generally more difficult in an open-set setting which is typical for surveillance scenarios, owing to a large number of non-target people (distractors) appearing open spaced scenes. This is evidently so that on the new Surveillance FR Challenge, the top-performing CentreFace deep learning FR model on the MegaFace benchmark can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.

Connected through hyperedge HE-1 (induced by source paper "person re identification by contour sketch under moderate clothing change"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "part aligned bilinear representations for person re identification"
    - "polar transformer networks"
    - "deformable convolutional networks"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "robust depth based person re identification"

Connected through hyperedge HE-2 (induced by source paper "fan feature adaptation network for surveillance face recognition and normalization"):
  with members:
    - "a two step disentanglement method"
    - "srobb targeted perceptual loss for single image super resolution"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"

Connected through hyperedge HE-3 (induced by source paper "characteristic regularisation for super resolving face images"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "super identity convolutional neural network for face hallucination"
    - "low resolution face recognition"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "sigan siamese generative adversarial network for identity preserving face hallucination"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "pixel recursive super resolution"
    - "learning to hallucinate face images via component generation and enhancement"
    - "deep joint face hallucination and recognition"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "cross resolution learning for face recognition"):
  with members:
    - "super identity convolutional neural network for face hallucination"
    - "deep face recognition a survey"
    - "low resolution face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

Connected through hyperedge HE-6 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "low resolution face recognition"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "s 3 fd single shot scale invariant face detector"

Connected through hyperedge HE-7 (induced by source paper "low resolution face recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "level playing field for million scale face recognition"
    - "studying very low resolution recognition using deep networks"
    - "finding tiny faces"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "umdfaces an annotated face dataset for training deep networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "beyond frontal faces improving person recognition using multiple cues"

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)