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: "an infinite latent attribute model for network data"
  Abstract: Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Connected through hyperedge HE-1 (induced by source paper "network structure exploration via bayesian nonparametric models"):
  with members:
    - "overlapping community detection in networks with positive and negative links"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "finding missing edges in networks based on their community structure"
    - "nonparametric link prediction in dynamic networks"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "experience versus talent shapes the structure of the web"

Connected through hyperedge HE-2 (induced by source paper "collapsed variational bayes inference of infinite relational model"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "community detection in networks with node attributes"
    - "on smoothing and inference for topic models"
    - "fast variational inference in the conjugate exponential family"
    - "rethinking collapsed variational bayes inference for lda"

Connected through hyperedge HE-3 (induced by source paper "a network model characterized by a latent attribute structure with competition"):
  with members:
    - "a weighted correlation index for rankings with ties"
    - "nonparametric link prediction in dynamic networks"
    - "infinite multiple membership relational modeling for complex networks"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"

Connected through hyperedge HE-4 (induced by source paper "joint inference of multiple label types in large networks"):
  with members:
    - "scaling graph based semi supervised learning to large number of labels using count min sketch"
    - "discriminative probabilistic models for relational data"
    - "sparse projections onto the simplex"

Connected through hyperedge HE-5 (induced by source paper "accams additive co clustering to approximate matrices succinctly"):
  with members:
    - "vog summarizing and understanding large graphs"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "iterative row sampling"

Connected through hyperedge HE-6 (induced by source paper "nonparametric multi group membership model for dynamic networks"):
  with members:
    - "latent multi group membership graph model"
    - "infinite multiple membership relational modeling for complex networks"
    - "modeling social networks with node attributes using the multiplicative attribute graph model"
    - "multiplicative attribute graph model of real world networks"
    - "supervised random walks predicting and recommending links in social 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.LG(Machine Learning)