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: "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks"
  Abstract: Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.

Connected through hyperedge HE-1 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-2 (induced by source paper "mining user behaviour from smartphone data a literature review"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "deeptransport learning spatial temporal dependency for traffic condition forecasting"
    - "smartphone based vehicle telematics a ten year anniversary"
    - "semmatch road semantics based accurate map matching for challenging positioning data"

Connected through hyperedge HE-3 (induced by source paper "faster fusion analytics for public transport event response"):
  with members:
    - "robust commuter movement inference from connected mobile devices"
    - "stochastic on time arrival problem in transit networks"
    - "early warning of human crowds based on query data from baidu maps analysis based on shanghai stampede"

Connected through hyperedge HE-4 (induced by source paper "representation learning of pedestrian trajectories using actor critic sequence to sequence autoencoder"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "an actor critic algorithm for sequence prediction"
    - "unsupervised learning of video representations using lstms"
    - "dueling network architectures for deep reinforcement learning"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "transportation modes classification using feature engineering"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "predicting crime using spatial features"
    - "feature selection a data perspective"
    - "spatio temporal data mining a survey of problems and methods"

Connected through hyperedge HE-6 (induced by source paper "a multi task deep learning architecture for maritime surveillance using ais data streams"):
  with members:
    - "filtering variational objectives"
    - "a recurrent latent variable model for sequential data"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "predicting transportation modes of gps trajectories using feature engineering and noise removal"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"

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)