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: "cross view semantic segmentation for sensing surroundings"
  Abstract: Sensing surroundings is ubiquitous and effortless to humans: It takes a single glance to extract the spatial configuration of objects and the free space from the scene. To help machine vision with spatial understanding capabilities, we introduce the View Parsing Network (VPN) for cross-view semantic segmentation. In this framework, the first-view observations are parsed into a top-down-view semantic map indicating precise object locations. VPN contains a view transformer module, designed to aggregate the first-view observations taken from multiple angles and modalities, in order to draw a bird-view semantic map. We evaluate the VPN framework for cross-view segmentation on two types of environments, indoors and driving-traffic scenes. Experimental results show that our model accurately predicts the top-down-view semantic mask of the visible objects from the first-view observations, as well as infer the location of contextually-relevant objects even if they are invisible.

Connected through hyperedge HE-1 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "weakly supervised silhouette based semantic change detection"

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)