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: "brain4cars car that knows before you do via sensory fusion deep learning architecture"
  Abstract: Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. #R##N#In this work we propose a vehicular sensor-rich platform and learning algorithms for maneuver anticipation. For this purpose we equip a car with cameras, Global Positioning System (GPS), and a computing device to capture the driving context from both inside and outside of the car. In order to anticipate maneuvers, we propose a sensory-fusion deep learning architecture which jointly learns to anticipate and fuse multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We propose a novel training procedure which allows the network to predict the future given only a partial temporal context. We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds before they occur in real-time with a precision and recall of 90.5\% and 87.4\% respectively.

Connected through hyperedge HE-1 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-2 (induced by source paper "real time driver state monitoring using a cnn based spatio temporal approach"):
  with members:
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "real time distracted driver posture classification"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "deep neural networks with auxiliary model regulated gating for resilient multi modal sensor fusion"):
  with members:
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "sensor fusion for robot control through deep reinforcement learning"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "multi view 3d object detection network for autonomous driving"
    - "fast r cnn"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-4 (induced by source paper "viena2 a driving anticipation dataset"):
  with members:
    - "agreeing to cross how drivers and pedestrians communicate"
    - "encouraging lstms to anticipate actions very early"
    - "playing for benchmarks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "densely connected convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "online localization and prediction of actions and interactions"

Connected through hyperedge HE-5 (induced by source paper "optimized gated deep learning architectures for sensor fusion"):
  with members:
    - "sensor fusion for robot control through deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "deep learning in robotics a review of recent research"):
  with members:
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep exploration via bootstrapped dqn"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "vime variational information maximizing exploration"
    - "achieving synergy in cognitive behavior of humanoids via deep learning of dynamic visuo motor attentional coordination"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "characterizing driving styles with deep learning"):
  with members:
    - "an empirical evaluation of deep learning on highway driving"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

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)