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: "interpretable latent spaces for learning from demonstration"
  Abstract: Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world. Models such as deep neural networks, with high capacity through their large parameter spaces, can be used to compress the high-dimensional sensory data to lower dimensional representations. These low-dimensional representations facilitate symbol grounding, but may not guarantee that the representation would be human-interpretable. We propose a method which utilises the grouping of user-defined symbols and their corresponding sensory observations in order to align the learnt compressed latent representation with the semantic notions contained in the abstract labels. We demonstrate this through experiments with both simulated and real-world object data, showing that such alignment can be achieved in a process of physical symbol grounding.

Connected through hyperedge HE-1 (induced by source paper "neural embedding for physical manipulations"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "normalized diversification"
    - "hierarchical visuomotor control of humanoids"
    - "spectral normalization for generative adversarial networks"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "neural probabilistic motor primitives for humanoid control"

Connected through hyperedge HE-2 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-3 (induced by source paper "vid2param modelling of dynamics parameters from video"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "active localization of gas leaks using fluid simulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "the orca hub explainable offshore robotics through intelligent interfaces"

Connected through hyperedge HE-4 (induced by source paper "vid2param online system identification from video for robotics applications"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "neural lander stable drone landing control using learned dynamics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-5 (induced by source paper "disentangled relational representations for explaining and learning from demonstration"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "monet unsupervised scene decomposition and representation"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "multi object representation learning with iterative variational inference"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "isolating sources of disentanglement in variational autoencoders"
    - "unsupervised learning of disentangled representations from video"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-6 (induced by source paper "embcomp visual interactive comparison of vector embeddings"):
  with members:
    - "visual designs for binned aggregation of multi class scatterplots"
    - "unsupervised hyperalignment for multilingual word embeddings"
    - "deep contextualized word representations"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "embeddingvis a visual analytics approach to comparative network embedding inspection"
    - "advances in pre training distributed word representations"
    - "node2vec scalable feature learning for networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"

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)