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: "toward a multilevel representation of protein molecules"
  Abstract: Computational Intelligence methods are typically designed according to the assumption that the input space is essentially a vector space. When departing from vector-based pattern representations many theoretical and practical problems arise, which are mostly due to the absence of an intuitive geometric interpretation of the data. However, since such representations could offer additional insights when used in real-world applications of data-driven inference systems, their exploitation is also a practical and convenient choice. Here we apply several state-of-the-art classification methods for non-geometric data, with the aim to compare different representations of the proteins gathered from Niwa et?al. (2009) 35. Such representations include sequences of objects and labeled (contact) graphs enriched with chemico-physical attributes. The experiment performed by Niwa et?al. provides the unique possibility to analyze the relative aggregation/folding propensity of the elements of the entire Escherichia coli (E. coli) proteome in a cell-free, standardized microenvironment. By this comparison, we are able to identify also some interesting general properties of proteins. Notably, (i) we suggest a threshold around 250 residues discriminating "easily foldable" from "hardly foldable" molecules consistent with other independent experiments, and (ii) we highlight the relevance of contact graph spectra for folding behavior discrimination and characterization of the E. coli solubility data. The soundness of the experimental results presented in this paper is proved by the statistically relevant relationships discovered among the chemico-physical description of proteins and the developed cost matrix of substitution that we used in the various discrimination systems.

Connected through hyperedge HE-1 (induced by source paper "one class classifiers based on entropic spanning graphs"):
  with members:
    - "fast and simple computation of top k closeness centralities"
    - "entropic one class classifiers"
    - "modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one class classification"
    - "a review of feature selection methods based on mutual information"

Connected through hyperedge HE-2 (induced by source paper "designing labeled graph classifiers by exploiting the renyi entropy of the dissimilarity representation"):
  with members:
    - "entropic one class classifiers"
    - "building pattern recognition applications with the spare library"

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.CE(Computational Engineering, Finance, and Science)