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: "surveillance video parsing with single frame supervision"
  Abstract: Surveillance video parsing, which segments the video frames into several labels, e.g., face, pants, left-leg, has wide applications. However,pixel-wisely annotating all frames is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage. To parse one particular frame, the video segment preceding the frame is jointly considered. SVP (1) roughly parses the frames within the video segment, (2) estimates the optical flow between frames and (3) fuses the rough parsing results warped by optical flow to produce the refined parsing result. The three components of SVP, namely frame parsing, optical flow estimation and temporal fusion are integrated in an end-to-end manner. Experimental results on two surveillance video datasets show the superiority of SVP over state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "cross domain human parsing via adversarial feature and label adaptation"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "self adversarial training for human pose estimation"
    - "be your own prada fashion synthesis with structural coherence"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "joint multi person pose estimation and semantic part segmentation"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "video deblurring via semantic segmentation and pixel wise non linear kernel"):
  with members:
    - "optical flow with semantic segmentation and localized layers"
    - "deep video deblurring"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "matching cnn meets knn quasi parametric human parsing"
    - "deep human parsing with active template regression"
    - "hand held video deblurring via efficient fourier aggregation"
    - "generalized video deblurring for dynamic scenes"
    - "learning to deblur"

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)