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: "recognizing car fluents from video"
  Abstract: Physical fluents, a term originally used by Newton [40], refers to time-varying object states in dynamic scenes. In this paper, we are interested in inferring the fluents of vehicles from video. For example, a door (hood, trunk) is open or closed through various actions, light is blinking to turn. Recognizing these fluents has broad applications, yet have received scant attention in the computer vision literature. Car fluent recognition entails a unified framework for car detection, car part localization and part status recognition, which is made difficult by large structural and appearance variations, low resolutions and occlusions. This paper learns a spatial-temporal And-Or hierarchical model to represent car fluents. The learning of this model is formulated under the latent structural SVM framework. Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents. In experiments, the proposed method outperforms several highly related baseline methods in terms of car fluent recognition and car part localization.

Connected through hyperedge HE-1 (induced by source paper "autonomous vehicles that interact with pedestrians a survey of theory and practice"):
  with members:
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "intent communication between autonomous vehicles and pedestrians"
    - "joint attention in autonomous driving jaad"

Connected through hyperedge HE-2 (induced by source paper "a causal and or graph model for visibility fluent reasoning in tracking interacting objects"):
  with members:
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "convolutional pose machines"
    - "what players do with the ball a physically constrained interaction modeling"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "beyond holistic object recognition enriching image understanding with part states"):
  with members:
    - "learning social affordance for human robot interaction"
    - "visual relationship detection with language priors"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "learning deconvolution network for semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"

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)