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 based significance of itemsets"
  Abstract: We consider the problem of defining the significance of an itemset. We say that the itemset is significant if we are surprised by its frequency when compared to the frequencies of its sub-itemsets. In other words, we estimate the frequency of the itemset from the frequencies of its sub-itemsets and compute the deviation between the real value and the estimate. For the estimation we use Maximum Entropy and for measuring the deviation we use Kullback-Leibler divergence. A major advantage compared to the previous methods is that we are able to use richer models whereas the previous approaches only measure the deviation from the independence model. We show that our measure of significance goes to zero for derivable itemsets and that we can use the rank as a statistical test. Our empirical results demonstrate that for our real datasets the independence assumption is too strong but applying more flexible models leads to good results.

Connected through hyperedge HE-1 (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 models and subjective interestingness an application to tiles in binary databases"

Connected through hyperedge HE-2 (induced by source paper "finding robust itemsets under subsampling"):
  with members:
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"

Connected through hyperedge HE-3 (induced by source paper "summarizing data succinctly with the most informative itemsets"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "using background knowledge to rank itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "probably the best itemsets"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-4 (induced by source paper "tell me what i need to know succinctly summarizing data with itemsets"):
  with members:
    - "using background knowledge to rank itemsets"
    - "probably the best itemsets"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-5 (induced by source paper "probably the best itemsets"):
  with members:
    - "finding good itemsets by packing data"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-6 (induced by source paper "using background knowledge to rank itemsets"):
  with members:
    - "safe projections of binary data sets"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy models and subjective interestingness an application to tiles in binary databases"):
  with members:
    - "explicit probabilistic models for databases and networks"

Connected through hyperedge HE-8 (induced by source paper "finding good itemsets by packing data"):
  with members:
    - "mining all non derivable frequent 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.LG(Machine Learning)