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: "predictive overlapping co clustering"
  Abstract: In the past few years co-clustering has emerged as an important data mining tool for two way data analysis. Co-clustering is more advantageous over traditional one dimensional clustering in many ways such as, ability to find highly correlated sub-groups of rows and columns. However, one of the overlooked benefits of co-clustering is that, it can be used to extract meaningful knowledge for various other knowledge extraction purposes. For example, building predictive models with high dimensional data and heterogeneous population is a non-trivial task. Co-clusters extracted from such data, which shows similar pattern in both the dimension, can be used for a more accurate predictive model building. Several applications such as finding patient-disease cohorts in health care analysis, finding user-genre groups in recommendation systems and community detection problems can benefit from co-clustering technique that utilizes the predictive power of the data to generate co-clusters for improved data analysis. #R##N#In this paper, we present the novel idea of Predictive Overlapping Co-Clustering (POCC) as an optimization problem for a more effective and improved predictive analysis. Our algorithm generates optimal co-clusters by maximizing predictive power of the co-clusters subject to the constraints on the number of row and column clusters. In this paper precision, recall and f-measure have been used as evaluation measures of the resulting co-clusters. Results of our algorithm has been compared with two other well-known techniques - K-means and Spectral co-clustering, over four real data set namely, Leukemia, Internet-Ads, Ovarian cancer and MovieLens data set. The results demonstrate the effectiveness and utility of our algorithm POCC in practice.

Connected through hyperedge HE-1 (induced by source paper "using gaussian measures for efficient constraint based clustering"):
  with members:
    - "understanding co evolution in large multi relational 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)