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: "recent advances in imitation learning from observation"
  Abstract: Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task. Conventionally, the imitator has access to both state and action information generated by an expert performing the task (e.g., the expert may provide a kinesthetic demonstration of object placement using a robotic arm). However, requiring the action information prevents imitation learning from a large number of existing valuable learning resources such as online videos of humans performing tasks. To overcome this issue, the specific problem of imitation from observation (IfO) has recently garnered a great deal of attention, in which the imitator only has access to the state information (e.g., video frames) generated by the expert. In this paper, we provide a literature review of methods developed for IfO, and then point out some open research problems and potential future work.

Connected through hyperedge HE-1 (induced by source paper "domain adversarial and conditional state space model for imitation learning"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "learning belief representations for imitation learning in pomdps"
    - "learning robotic manipulation through visual planning and acting"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "learning coupled policies for simultaneous machine translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "thinking slow about latency evaluation for simultaneous machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "low latency neural speech translation"

Connected through hyperedge HE-3 (induced by source paper "leveraging human guidance for deep reinforcement learning tasks"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "atari head atari human eye tracking and demonstration dataset"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "agil learning attention from human for visuomotor tasks"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "learning actionable representations from visual observations"
    - "generative adversarial imitation from observation"

Connected through hyperedge HE-4 (induced by source paper "faster and safer training by embedding high level knowledge into deep reinforcement learning"):
  with members:
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "hierarchical imitation and reinforcement learning"
    - "parameter transfer unit for deep neural networks"
    - "interactive learning from policy dependent human feedback"
    - "constrained policy optimization"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "safe reinforcement learning via shielding"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-5 (induced by source paper "imitation learning from video by leveraging proprioception"):
  with members:
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "generative adversarial imitation from observation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-6 (induced by source paper "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"):
  with members:
    - "what object should i use task driven object detection"
    - "scene graph generation with external knowledge and image reconstruction"
    - "robocse robot common sense embedding"
    - "integrating knowledge and reasoning in image understanding"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-7 (induced by source paper "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"):
  with members:
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "learning actionable representations from visual observations"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "deep reinforcement learning that matters"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-8 (induced by source paper "sample efficient adversarial imitation learning from observation"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "generative adversarial imitation from observation"
    - "multi agent generative adversarial imitation learning"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "third person imitation learning"
    - "improved training of wasserstein gans"

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.RO(Robotics)