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: "obstacle avoidance using stereo camera"
  Abstract: In this paper we present a novel method for obstacle avoidance using the stereo camera. The conventional obstacle avoidance methods and their limitations are discussed. A new algorithm is developed for the real-time obstacle avoidance which responds faster to unexpected obstacles. In this approach the depth map is divided into optimized number of regions and the minimum depth at each section is assigned as the depth of that region. A fuzzy controller is designed to create the drive commands for the robot/quadcopter. The system was tested on multiple paths with different obstacles and the results demonstrated the high accuracy of the developed system.

Connected through hyperedge HE-1 (induced by source paper "learning depth with convolutional spatial propagation network"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "single view stereo matching"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"

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)