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: "implementation of hand detection based techniques for human computer interaction"
  Abstract: The computer industry is developing at a fast pace. With this development almost all of the fields under computers have advanced in the past couple of decades. But the same technology is being used for human computer interaction that was used in 1970s. Even today the same type of keyboard and mouse is used for interacting with computer systems. With the recent boom in the mobile segment touchscreens have become popular for interaction with cell phones. But these touchscreens are rarely used on traditional systems. This paper tries to introduce methods for human computer interaction using the users hand which can be used both on traditional computer platforms as well as cell phones. The methods explain how the users detected hand can be used as input for applications and also explain applications that can take advantage of this type of interaction mechanism.

Connected through hyperedge HE-1 (induced by source paper "detector in detector multi level analysis for human parts"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "aggregated channels network for real time pedestrian detection"
    - "fssd feature fusion single shot multibox detector"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "a jointly learned deep architecture for facial attribute analysis and face detection in the wild"
    - "citypersons a diverse dataset for pedestrian detection"
    - "faceness net face detection through deep facial part responses"

Connected through hyperedge HE-2 (induced by source paper "hand gesture recognition based text input method for ar vr wearable devices"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)