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: "towards grounding conceptual spaces in neural representations"
  Abstract: The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. It aims at bridging the gap between symbolic and subsymbolic processing. Instances are represented by points in a high-dimensional space and concepts are represented by convex regions in this space. In this paper, we present our approach towards grounding the dimensions of a conceptual space in latent spaces learned by an InfoGAN from unlabeled data.

Connected through hyperedge HE-1 (induced by source paper "formalized conceptual spaces with a geometric representation of correlations"):
  with members:
    - "a thorough formalization of conceptual spaces"
    - "a comprehensive implementation of conceptual spaces"
    - "measuring relations between concepts in conceptual spaces"
    - "the size of a hyperball in a conceptual space"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "representation learning a review and new perspectives"
    - "the symbol grounding problem"

Connected through hyperedge HE-2 (induced by source paper "unsupervised disentangled representation learning with analogical relations"):
  with members:
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "unsupervised learning of disentangled representations from video"
    - "interpretable transformations with encoder decoder networks"
    - "improved training of wasserstein gans"
    - "instance normalization the missing ingredient for fast stylization"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)