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: "the nonparametric metadata dependent relational model"
  Abstract: We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships to depend on, and be predicted from, arbitrary node metadata. We develop efficient MCMC algorithms for learning NMDR models from partially observed node relationships. Retrospective MCMC methods allow our sampler to work directly with the infinite stick-breaking representation of the NMDR, avoiding the need for finite truncations. Our results demonstrate recovery of useful latent communities from real-world social and ecological networks, and the usefulness of metadata in link prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "struct mmsb mixed membership stochastic blockmodels with interpretable structured priors"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "snap a general purpose network analysis and graph mining library"
    - "discriminative nonparametric latent feature relational models with data augmentation"
    - "spectral networks and locally connected networks on graphs"
    - "community detection in networks with node attributes"
    - "latent multi group membership graph model"

Connected through hyperedge HE-2 (induced by source paper "adapting stochastic block models to power law degree distributions"):
  with members:
    - "estimating the number of communities in a network"
    - "role discovery in networks"
    - "model selection for degree corrected block models"
    - "dynamic infinite mixed membership stochastic blockmodel"
    - "oriented and degree generated block models generating and inferring communities with inhomogeneous degree distributions"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "friendship prediction in composite social networks"):
  with members:
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "composite social network for predicting mobile apps installation"
    - "supervised random walks predicting and recommending links in social networks"
    - "domain adaptation for statistical classifiers"

Connected through hyperedge HE-4 (induced by source paper "dynamic infinite mixed membership stochastic blockmodel"):
  with members:
    - "dependent hierarchical normalized random measures for dynamic topic modeling"

Connected through hyperedge HE-5 (induced by source paper "dynamic multi relational chinese restaurant process for analyzing influences on users in social media"):
  with members:
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"

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.LG(Machine Learning)