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: "face detection with a 3d model"
  Abstract: Abstract   This chapter presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six-dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on difference features relative to the 3D pose. A compatible subset of candidates is then obtained by nonmaximal suppression. Experiments on the Face Detection Database (FDDB) and Annotated Faces in the Wild (AFW) face detection datasets show that the proposed 3D model-based approach obtains results comparable to the Cascade-CNN [ 17 ].

Connected through hyperedge HE-1 (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-2 (induced by source paper "s 3 fd single shot scale invariant face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"

Connected through hyperedge HE-3 (induced by source paper "faceboxes a cpu real time face detector with high accuracy"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "unitbox an advanced object detection network"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "compact convolutional neural network cascade for face detection"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "multi view face detection using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from facial parts responses to face detection a deep learning approach"
    - "going deeper with convolutions"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "aggregate channel features for multi view face detection"

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)