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: "deep reinforcement learning using capsules in advanced game environments"
  Abstract: Master's thesis Information- and communication technology IKT590 - University of Agder 2017

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"
    - "compositional coding capsule network with k means routing for text classification"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-2 (induced by source paper "capsule network performance with autonomous navigation"):
  with members:
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "advanced capsule networks via context awareness"
    - "deep reinforcement learning"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "simultaneous localization and mapping with depth prediction using capsule networks for uavs"
    - "state representation learning with recurrent capsule networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "interpretable convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "capsule networks need an improved routing algorithm"):
  with members:
    - "capsnet comparative performance evaluation for image classification"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "improved explainability of capsule networks relevance path by agreement"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "dropmax adaptive variational softmax"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.AI(Artificial Intelligence)