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: "sface an efficient network for face detection in large scale variations"
  Abstract: Face detection serves as a fundamental research topic for many applications like face recognition. Impressive progress has been made especially with the recent development of convolutional neural networks. However, the issue of large scale variations, which widely exists in high resolution images/videos, has not been well addressed in the literature. In this paper, we present a novel algorithm called SFace, which efficiently integrates the anchor-based method and anchor-free method to address the scale issues. A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations. The SFace architecture shows promising results on the new 4K-Face benchmarks. In addition, our method can run at 50 frames per second (fps) with an accuracy of 80% AP on the standard WIDER FACE dataset, which outperforms the state-of-art algorithms by almost one order of magnitude in speed while achieves comparative performance.

Connected through hyperedge HE-1 (induced by source paper "feature selective anchor free module for single shot object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "seeing small faces from robust anchor s perspective"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "detnet a backbone network for object detection"
    - "ring loss convex feature normalization for face recognition"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "pyramidbox high performance detector for finding tiny face"):
  with members:
    - "region proposal by guided anchoring"
    - "mask scoring r cnn"
    - "dsfd dual shot face detector"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "autofocus efficient multi scale inference"
    - "metaanchor learning to detect objects with customized anchors"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-3 (induced by source paper "wider face and pedestrian challenge 2018 methods and results"):
  with members:
    - "person search in videos with one portrait through visual and temporal links"
    - "group normalization"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "unifying identification and context learning for person recognition"
    - "soft nms improving object detection with one line of code"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-4 (induced by source paper "dubox no prior box objection detection via residual dual scale detectors"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "derpn taking a further step toward more general object detection"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "metaanchor learning to detect objects with customized anchors"):
  with members:
    - "detnet a backbone network for object detection"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "fssd feature fusion single shot multibox detector"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "what can help pedestrian detection"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-6 (induced by source paper "improving face detection performance with 3d rendered synthetic data"):
  with members:
    - "survey of face detection on low quality images"
    - "training deep face recognition systems with synthetic data"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "ganimation anatomically aware facial animation from a single image"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "recent progress of face image synthesis"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"

Connected through hyperedge HE-7 (induced by source paper "decoupled weight decay regularization"):
  with members:
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "autoaugment learning augmentation policies from data"
    - "three mechanisms of weight decay regularization"
    - "intracranial error detection via deep learning"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "snapshot ensembles train 1 get m for free"

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)