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: "loosely coupled semi direct monocular slam"
  Abstract: We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizations: 1) photometric bundle adjustment (BA) that jointly optimizes the local structure and motion, 2) geometric BA that refines keyframe poses and associated feature map points, and 3) pose graph optimization to achieve global map consistency in the presence of loop closures. This is achieved in real-time by limiting the feature-based operations to marginalized keyframes from the direct odometry module. Exhaustive evaluation on two benchmark datasets demonstrates that our system outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "condition invariant multi view place recognition"):
  with members:
    - "single view place recognition under seasonal changes"
    - "deep learning features at scale for visual place recognition"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "deep residual learning for image recognition"
    - "training a convolutional neural network for appearance invariant place recognition"
    - "on the performance of convnet features for place recognition"

Connected through hyperedge HE-2 (induced by source paper "a unified formulation for visual odometry"):
  with members:
    - "fdmo feature assisted direct monocular odometry"
    - "ldso direct sparse odometry with loop closure"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "keyframe based monocular slam"
    - "direct sparse odometry"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "robust semi direct monocular visual odometry using edge and illumination robust cost"):
  with members:
    - "semantic nearest neighbor fields monocular edge visual odometry"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "edge slam edge points based monocular visual slam"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "holistically nested edge detection"
    - "orb slam a versatile and accurate monocular slam system"

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)