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: "to boost or not to boost on the limits of boosted trees for object detection"
  Abstract: We aim to study the modeling limitations of the commonly employed boosted decision trees classifier. Inspired by the success of large, data-hungry visual recognition models (e.g. deep convolutional neural networks), this paper focuses on the relationship between modeling capacity of the weak learners, dataset size, and dataset properties. A set of novel experiments on the Caltech Pedestrian Detection benchmark results in the best known performance among non-CNN techniques while operating at fast run-time speed. Furthermore, the performance is on par with deep architectures (9.71% log-average miss rate), while using only HOG+LUV channels as features. The conclusions from this study are shown to generalize over different object detection domains as demonstrated on the FDDB face detection benchmark (93.37% accuracy). Despite the impressive performance, this study reveals the limited modeling capacity of the common boosted trees model, motivating a need for architectural changes in order to compete with multi-level and very deep architectures.

Connected through hyperedge HE-1 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "on learning 3d face morphable model from in the wild images"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-2 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-3 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-4 (induced by source paper "dafe fd density aware feature enrichment for face detection"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "accurate face detection for high performance"):
  with members:
    - "robust and high performance face detector"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "pyramidbox high performance detector for finding tiny face"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"

Connected through hyperedge HE-6 (induced by source paper "relational learning for joint head and human detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "msfd multi scale receptive field face detector"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "ssh single stage headless face detector"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "sfa small faces attention face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "pose invariant face alignment with a single cnn"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "faceboxes a cpu real time face detector with high accuracy"

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)