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: "maximum entropy models and subjective interestingness an application to tiles in binary databases"
  Abstract: Recent research has highlighted the practical benefits of subjective interestingness measures, which quantify the novelty or unexpectedness of a pattern when contrasted with any prior information of the data miner (Silberschatz and Tuzhilin, 1995; Geng and Hamilton, 2006). A key challenge here is the formalization of this prior information in a way that lends itself to the definition of an interestingness subjective measure that is both meaningful and practical. #R##N#In this paper, we outline a general strategy of how this could be achieved, before working out the details for a use case that is important in its own right. #R##N#Our general strategy is based on considering prior information as constraints on a probabilistic model representing the uncertainty about the data. More specifically, we represent the prior information by the maximum entropy (MaxEnt) distribution subject to these constraints. We briefly outline various measures that could subsequently be used to contrast patterns with this MaxEnt model, thus quantifying their subjective interestingness.

Connected through hyperedge HE-1 (induced by source paper "scalable dyadic independence models with local and global constraints"):
  with members:
    - "from community to role based graph embeddings"
    - "evalne a framework for evaluating network embeddings on link prediction"
    - "sample optimal low rank approximation of distance matrices"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "explainable subgraphs with surprising densities a subgroup discovery approach"):
  with members:
    - "social structure of facebook networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "comparing apples and oranges measuring differences between data mining results"):
  with members:
    - "distances between data sets based on summary statistics"
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "an approximation ratio for biclustering"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-4 (induced by source paper "discovering interesting cycles in directed graphs"):
  with members:
    - "evaluating balance on social networks from their simple cycles"
    - "faster parametric shortest path and minimum balance algorithms"

Connected through hyperedge HE-5 (induced by source paper "discovering descriptive tile trees by mining optimal geometric subtiles"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "are your items in order"

Connected through hyperedge HE-6 (induced by source paper "the primping routine tiling through proximal alternating linearized minimization"):
  with members:
    - "comparing apples and oranges measuring differences between exploratory data mining results"

Connected through hyperedge HE-7 (induced by source paper "a survey of utility oriented pattern mining"):
  with members:
    - "huopm high utility occupancy pattern mining"
    - "proum projection based utility mining on sequence data"
    - "fast utility mining on complex sequences"
    - "a survey of parallel sequential pattern mining"
    - "privacy preserving utility mining a survey"
    - "mining all non derivable frequent itemsets"

Connected through hyperedge HE-8 (induced by source paper "efficiently discovering locally exceptional yet globally representative subgroups"):
  with members:
    - "identifying consistent statements about numerical data with dispersion corrected subgroup discovery"
    - "inherent trade offs in the fair determination of risk scores"
    - "summarizing data succinctly with the most informative itemsets"
    - "maximum entropy based significance of itemsets"

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.AI(Artificial Intelligence)