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: "long term on board prediction of people in traffic scenes under uncertainty"
  Abstract: Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.

Connected through hyperedge HE-1 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "learning trajectory dependencies for human motion prediction"
    - "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"
    - "imitation learning for human pose prediction"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "spatio temporal manifold learning for human motions via long horizon modeling"

Connected through hyperedge HE-2 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (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-5 (induced by source paper "unsupervised traffic accident detection in first person videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "classifying collisions with spatio temporal action graph networks"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"

Connected through hyperedge HE-6 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "stochastic adversarial video prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting action tubes"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-7 (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"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "future person localization in first person videos"
    - "simple online and realtime tracking with a deep association metric"

Connected through hyperedge HE-8 (induced by source paper "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "object detection with deep learning a review"

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.CV(Computer Vision and Pattern Recognition)