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: "joint attention in driver pedestrian interaction from theory to practice"
  Abstract: Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. Such a task requires communication between autonomous vehicles and other road users in order to resolve various traffic ambiguities. The interaction between road users is a form of negotiation in which the parties involved have to share their attention regarding a common objective or a goal (e.g. crossing an intersection), and coordinate their actions in order to accomplish it. In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view. More specifically, we will discuss the theoretical background behind joint attention, its application to traffic interaction and practical approaches to implementing joint attention for autonomous vehicles.

Connected through hyperedge HE-1 (induced by source paper "looking to relations for future trajectory forecast"):
  with members:
    - "dynamic channel a planning framework for crowd navigation"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "human action recognition and prediction a survey"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-2 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-3 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "learning to drive using inverse reinforcement learning and deep q networks"

Connected through hyperedge HE-4 (induced by source paper "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "real world anomaly detection in surveillance videos"
    - "is it safe to drive an overview of factors challenges and datasets for driveability assessment in autonomous driving"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-5 (induced by source paper "visual attention and its intimate links to spatial cognition"):
  with members:
    - "a review of 40 years of cognitive architecture research core cognitive abilities and practical applications"
    - "revisiting active perception"

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.RO(Robotics)