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: "convolutional neural network for trajectory prediction"
  Abstract: Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.

Connected through hyperedge HE-1 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction"):
  with members:
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-2 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "scene compliant trajectory forecast with agent centric spatio temporal grids"):
  with members:
    - "scene induced multi modal trajectory forecasting via planning"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "human motion trajectory prediction a survey"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "predicting motion of vulnerable road users using high definition maps and efficient convnets"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-5 (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"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "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-6 (induced by source paper "stochastic sampling simulation for pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-7 (induced by source paper "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "car net clairvoyant attentive recurrent network"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-8 (induced by source paper "real time trajectory prediction using deep conditional generative models"):
  with members:
    - "reliable real time ball tracking for robot table tennis"
    - "adaptation and robust learning of probabilistic movement primitives"
    - "long term forecasting using higher order tensor rnns"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "tensorflow a system for large scale machine learning"

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)