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: "learning recurrent representations for hierarchical behavior modeling"
  Abstract: We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules.

Connected through hyperedge HE-1 (induced by source paper "learning calibratable policies using programmatic style consistency"):
  with members:
    - "deepmind control suite"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "adversarial information factorization"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-2 (induced by source paper "generating multi agent trajectories using programmatic weak supervision"):
  with members:
    - "robust imitation of diverse behaviors"
    - "snorkel rapid training data creation with weak supervision"
    - "coordinated multi agent imitation learning"
    - "generating long term trajectories using deep hierarchical networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "learning the structure of generative models without labeled data"
    - "generative adversarial imitation learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "generative multi agent behavioral cloning"):
  with members:
    - "coordinated multi agent imitation learning"
    - "generating long term trajectories using deep hierarchical networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "generative adversarial imitation learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "strategic attentive writer for learning macro actions"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "tracking all members of a honey bee colony over their lifetime"):
  with members:
    - "automatic localization and decoding of honeybee markers using deep convolutional neural networks"
    - "multiple object tracking a review"

Connected through hyperedge HE-5 (induced by source paper "recurrent ladder networks"):
  with members:
    - "semi supervised phoneme recognition with recurrent ladder networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "neural expectation maximization"
    - "on the exact relationship between the denoising function and the data distribution"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "feynman machine the universal dynamical systems computer"

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)