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: "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
  Abstract: Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution.

Connected through hyperedge HE-1 (induced by source paper "transformer networks for trajectory forecasting"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "generating long sequences with sparse transformers"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"

Connected through hyperedge HE-2 (induced by source paper "sr lstm state refinement for lstm towards pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "videos as space time region graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"

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 "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"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "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-5 (induced by source paper "drogon a causal reasoning framework for future trajectory forecast"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "looking to relations for future trajectory forecast"
    - "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"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-6 (induced by source paper "forecasting people trajectories and head poses by jointly reasoning on tracklets and vislets"):
  with members:
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "human trajectory prediction using spatially aware deep attention models"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"
    - "deep learning driven visual path prediction from a single image"

Connected through hyperedge HE-7 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "long range indoor navigation with prm rl"
    - "reward shaping via meta learning"
    - "deep visual mpc policy learning for navigation"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "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"

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)