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: "egocentric vision based future vehicle localization for intelligent driving assistance systems"
  Abstract: Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.

Connected through hyperedge HE-1 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-3 (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"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (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"
    - "classifying collisions with spatio temporal action graph networks"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "joint person segmentation and identification in synchronized first and third person videos"

Connected through hyperedge HE-5 (induced by source paper "forecasting pedestrian trajectory with machine annotated training data"):
  with members:
    - "human action recognition and prediction a survey"
    - "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"
    - "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-6 (induced by source paper "looking to relations for future trajectory forecast"):
  with members:
    - "dynamic channel a planning framework for crowd navigation"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "human action recognition and prediction a survey"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-7 (induced by source paper "nemo future object localization using noisy ego priors"):
  with members:
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "looking to relations for future trajectory forecast"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "identifying first person camera wearers in third person videos"

Connected through hyperedge HE-8 (induced by source paper "goal oriented object importance estimation in on road driving videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "learning rich features for image manipulation detection"
    - "temporal recurrent networks for online action detection"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting driver attention in critical situations"
    - "an analysis of scale invariance in object detection snip"
    - "dynamic zoom in network for fast object detection in large images"
    - "predicting the driver s focus of attention the dr eye ve project"

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)