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 episodic memory encoding recalling and predicting episodic experiences for robot action execution"
  Abstract: We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model 1) encodes observed actions in a latent vector space and, based on this latent encoding, 2) infers most similar episodes previously experienced, 3) reconstructs original episodes, and 4) predicts future frames in an end-to-end fashion. Results show that conceptually similar actions are mapped into the same region of the latent vector space. Based on these results, we introduce an action matching and retrieval mechanism, benchmark its performance on two large-scale action datasets, 20BN-something-something and ActivityNet and evaluate its generalization capability in a real-world scenario on a humanoid robot.

Connected through hyperedge HE-1 (induced by source paper "learning one shot imitation from humans without humans"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "deep neural network compression for image classification and object detection"):
  with members:
    - "model compression via distillation and quantization"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "yolov3 an incremental improvement"
    - "a survey on methods and theories of quantized neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "thinet a filter level pruning method for deep neural network compression"
    - "mask r cnn"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "one shot imitation from observing humans via domain adaptive meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "virtual to real reinforcement learning for autonomous driving"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to generalize meta learning for domain generalization"
    - "satellite image based localization via learned embeddings"
    - "learning robot activities from first person human videos using convolutional future regression"
    - "third person imitation learning"

Connected through hyperedge HE-4 (induced by source paper "introducing the simulated flying shapes and simulated planar manipulator datasets"):
  with members:
    - "unsupervised learning of video representations using lstms"

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)