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: "real time face recognition using adaboost improved fast pca algorithm"
  Abstract: This paper presents an automated system for human face recognition in a real time background world for a large homemade dataset of persons face. The task is very difficult as the real time background subtraction in an image is still a challenge. Addition to this there is a huge variation in human face image in terms of size, pose and expression. The system proposed collapses most of this variance. To detect real time human face AdaBoost with Haar cascade is used and a simple fast PCA and LDA is used to recognize the faces detected. The matched face is then used to mark attendance in the laboratory, in our case. This biometric system is a real time attendance system based on the human face recognition with a simple and fast algorithms and gaining a high accuracy rate..

Connected through hyperedge HE-1 (induced by source paper "face detection using deep learning an improved faster rcnn approach"):
  with members:
    - "face detection with end to end integration of a convnet and a 3d model"
    - "face detection with the faster r cnn"
    - "a fast and accurate unconstrained face detector"
    - "bootstrapping face detection with hard negative examples"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping face detection with hard negative examples"):
  with members:
    - "is faster r cnn doing well for pedestrian detection"
    - "face detection with the faster r cnn"
    - "training region based object detectors with online hard example mining"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "study of humanoid push recovery based on experiments"
    - "machine learning approach for text and document mining"
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

Connected through hyperedge HE-4 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "study of humanoid push recovery based on experiments"
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "machine learning approach for text and document mining"
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

Connected through hyperedge HE-5 (induced by source paper "noisy distance measurements using 3 d localization with rb rf methods"):
  with members:
    - "machine learning approach for text and document mining"
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

Connected through hyperedge HE-6 (induced by source paper "machine learning approach for text and document mining"):
  with members:
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

Connected through hyperedge HE-7 (induced by source paper "social and business intelligence analysis using pso"):
  with members:
    - "machine learning approach for text and document mining"

Connected through hyperedge HE-8 (induced by source paper "enhanced ezw technique for compression of image by setting detail retaining pass number"):
  with members:
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

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)