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: "determinantal clustering processes a nonparametric bayesian approach to kernel based semi supervised clustering"
  Abstract: Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Connected through hyperedge HE-1 (induced by source paper "learning beyond predefined label space via bayesian nonparametric topic modelling"):
  with members:
    - "bayesian nonexhaustive learning for online discovery and modeling of emerging classes"

Connected through hyperedge HE-2 (induced by source paper "efficient sampling for k determinantal point processes"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "notes on using determinantal point processes for clustering with applications to text clustering"
    - "diversifying sparsity using variational determinantal point processes"
    - "learning determinantal point processes"
    - "markov determinantal point processes"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-3 (induced by source paper "diversified hidden markov models for sequential labeling"):
  with members:
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "markov determinantal point processes"

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)