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: "predicting vehicle behaviors over an extended horizon using behavior interaction network"
  Abstract: Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.

Connected through hyperedge HE-1 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching"):
  with members:
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "on a formal model of safe and scalable self driving cars"
    - "despot online pomdp planning with regularization"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"):
  with members:
    - "fully featured attribute transfer"
    - "a multi domain feature learning method for visual place recognition"
    - "a closed form solution to universal style transfer"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "interpolation in generative models"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "interpreting the latent space of gans for semantic face editing"

Connected through hyperedge HE-3 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic interaction aware scene understanding for reinforcement learning in autonomous driving"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "dynamic input for deep reinforcement learning in autonomous driving"
    - "fast graph representation learning with pytorch geometric"
    - "graph neural networks a review of methods and applications"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "graph networks as learnable physics engines for inference and control"
    - "deep sets"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-5 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "recurrent neural processes"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "the functional neural process"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"
    - "a new multi vehicle trajectory generator to simulate vehicle to vehicle encounters"

Connected through hyperedge HE-6 (induced by source paper "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "carla an open urban driving simulator"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"

Connected through hyperedge HE-7 (induced by source paper "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "search based optimal motion planning for automated driving"
    - "baidu apollo em motion planner"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

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)