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: "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
  Abstract: In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effectiveness of various deep neural networks for predicting future pedestrian paths are evaluated. The analyzed deep networks solely rely, like in the traditional approaches, on observed tracklets without human-human interaction information. The evaluation is done on the publicly available TrajNet benchmark dataset, which builds up a repository of considerable and popular datasets for trajectory-based activity forecasting. We show that a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve sophisticated results compared to elaborated models in such scenarios. Further, we investigate failure cases and give explanations for observed phenomena and give some recommendations for overcoming demonstrated shortcomings.

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 "path planning in dynamic environments using generative rnns and monte carlo tree search"):
  with members:
    - "human motion trajectory prediction a survey"
    - "predicting responses to a robot s future motion using generative recurrent neural networks"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-3 (induced by source paper "future near collision prediction from monocular video feasibility dataset and challenges"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-4 (induced by source paper "forecasting people trajectories and head poses by jointly reasoning on tracklets and vislets"):
  with members:
    - "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"
    - "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-5 (induced by source paper "safecritic collision aware trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "car net clairvoyant attentive recurrent network"
    - "social attention modeling attention in human crowds"
    - "improved techniques for training gans"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "stochastic trajectory prediction with social graph network"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "stochastic variational video prediction"

Connected through hyperedge HE-7 (induced by source paper "predicting responses to a robot s future motion using generative recurrent neural networks"):
  with members:
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "social attention modeling attention in human crowds"
    - "socially aware motion planning with deep reinforcement learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"):
  with members:
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "a data driven model for interaction aware pedestrian motion prediction in object cluttered environments"

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)