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: "agreeing to cross how drivers and pedestrians communicate"
  Abstract: The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of pedestrian behaviors in various street configurations and weather conditions. These examples were selected from approx. 240 hours of driving in the city, suburban and urban roads. The second contribution is an analysis of our data from the point of view of joint attention. We identify what types of non-verbal communication cues road users use at the point of crossing, their responses, and under what circumstances the crossing event takes place. It was found that in more than 90% of the cases pedestrians gaze at the approaching cars prior to crossing in non-signalized crosswalks. The crossing action, however, depends on additional factors such as time to collision (TTC), explicit driver's reaction or structure of the crosswalk.

Connected through hyperedge HE-1 (induced by source paper "multiple object forecasting predicting future object locations in diverse environments"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "looking to relations for future trajectory forecast"
    - "online multi object tracking with dual matching attention networks"
    - "forecasting pedestrian trajectory with machine annotated training data"
    - "peeking into the future predicting future person activities and locations in videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "forecasting pedestrian trajectory with machine annotated training data"):
  with members:
    - "human action recognition and prediction a survey"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "exploring the limits of weakly supervised pretraining"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "future person localization in first person videos"
    - "simple online and realtime tracking with a deep association metric"

Connected through hyperedge HE-3 (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"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-4 (induced by source paper "intention recognition of pedestrians and cyclists by 2d pose estimation"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "an implementation of faster rcnn with study for region sampling"
    - "simple online and realtime tracking with a deep association metric"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "r cnns for pose estimation and action detection"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-5 (induced by source paper "gladas gesture learning for advanced driver assistance systems"):
  with members:
    - "real time hand gesture detection and classification using convolutional neural networks"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "carla an open urban driving simulator"
    - "intent communication between autonomous vehicles and pedestrians"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-6 (induced by source paper "eye contact between pedestrians and drivers"):
  with members:
    - "human centered autonomous vehicle systems principles of effective shared autonomy"

Connected through hyperedge HE-7 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "multi agent common knowledge reinforcement learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "cooperative and distributed reinforcement learning of drones for field coverage"
    - "multi vehicle flocking control with deep deterministic policy gradient method"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"

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)