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: "training effective node classifiers for cascade classification"
  Abstract: Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "anchor cascade for efficient face detection"):
  with members:
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection through scale friendly deep convolutional networks"
    - "scale aware face detection"
    - "recurrent scale approximation for object detection in cnn"
    - "faceness net face detection through deep facial part responses"
    - "ssh single stage headless face detector"
    - "face detection with end to end integration of a convnet and a 3d model"

Connected through hyperedge HE-2 (induced by source paper "occlusion aware r cnn detecting pedestrians in a crowd"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "single shot refinement neural network for object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "faceboxes a cpu real time face detector with high accuracy"

Connected through hyperedge HE-3 (induced by source paper "learning sampling distributions for efficient object detection"):
  with members:
    - "what makes for effective detection proposals"
    - "ten years of pedestrian detection what have we learned"
    - "how good are detection proposals really"
    - "pedestrian detection aided by deep learning semantic tasks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "active testing for face detection and localization"

Connected through hyperedge HE-4 (induced by source paper "cascade learning by optimally partitioning"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "ten years of pedestrian detection what have we learned"

Connected through hyperedge HE-5 (induced by source paper "strengthening the effectiveness of pedestrian detection with spatially pooled features"):
  with members:
    - "pedestrian detection with unsupervised multi stage feature learning"

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)