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: "a capsule network for traffic speed prediction in complex road networks"
  Abstract: This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error.

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"

Connected through hyperedge HE-2 (induced by source paper "efficiency and scalability of multi lane capsule networks mlcn"):
  with members:
    - "path capsule networks"
    - "capsnet comparative performance evaluation for image classification"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "fast capsnet for lung cancer screening"
    - "improved explainability of capsule networks relevance path by agreement"
    - "compositional coding capsule network with k means routing for text classification"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-3 (induced by source paper "structural recurrent neural network for traffic speed prediction"):
  with members:
    - "social attention modeling attention in human crowds"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "structural rnn deep learning on spatio temporal graphs"
    - "adam a method for stochastic optimization"

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)