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: "intent communication between autonomous vehicles and pedestrians"
  Abstract: When pedestrians encounter vehicles, they typically stop and wait for a signal from the driver to either cross or wait. What happens when the car is autonomous and there isn't a human driver to signal them? This paper seeks to address this issue with an intent communication system (ICS) that acts in place of a human driver. This intent system has been developed to take into account the psychology behind what pedestrians are familiar with and what they expect from machines. The system integrates those expectations into the design of physical systems and mathematical algorithms. The goal of the system is to ensure that communication is simple, yet effective without leaving pedestrians with a sense of distrust in autonomous vehicles. To validate the ICS, two types of experiments have been run: field tests with an autonomous vehicle to determine how humans actually interact with the ICS and simulations to account for multiple potential behaviors.The results from both experiments show that humans react positively and more predictably when the intent of the vehicle is communicated compared to when the intent of the vehicle is unknown. In particular, the results from the simulation specifically showed a 142 percent difference between the pedestrian's trust in the vehicle's actions when the ICS is enabled and the pedestrian has prior knowledge of the vehicle than when the ICS is not enabled and the pedestrian having no prior knowledge of the vehicle.

Connected through hyperedge HE-1 (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"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "carla an open urban driving simulator"
    - "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-2 (induced by source paper "a hybrid control design for autonomous vehicles at uncontrolled intersections"):
  with members:
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"

Connected through hyperedge HE-3 (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"
    - "recognizing car fluents from video"
    - "joint attention in autonomous driving jaad"

Connected through hyperedge HE-4 (induced by source paper "expressing robot incapability"):
  with members:
    - "communicating robot arm motion intent through mixed reality head mounted displays"
    - "expressive robot motion timing"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "exploring implicit human responses to robot mistakes in a learning from demonstration task"

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.HC(Human-Computer Interaction)