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: "normalized information distance"
  Abstract: The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.

Connected through hyperedge HE-1 (induced by source paper "one shot induction of generalized logical concepts via human guidance"):
  with members:
    - "low shot learning from imaginary data"
    - "matching networks for one shot learning"
    - "conditional kolmogorov complexity and universal probability"

Connected through hyperedge HE-2 (induced by source paper "towards automated boundary value testing with program derivatives and search"):
  with members:
    - "finding a boundary between valid and invalid regions of the input space"
    - "test set diameter quantifying the diversity of sets of test cases"

Connected through hyperedge HE-3 (induced by source paper "omega an architecture for ai unification"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "imagination augmented agents for deep reinforcement learning"
    - "video summarization with long short term memory"
    - "deep convolutional inverse graphics network"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "church a language for generative models"
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-4 (induced by source paper "universal psychometrics tasks difficulty composition and decomposition"):
  with members:
    - "definition and properties to assess multi agent environments as social intelligence tests"
    - "ai evaluation past present and future"
    - "an approximation of the universal intelligence measure"
    - "a monte carlo aixi approximation"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-5 (induced by source paper "dlolis a description logic based text ontology learning"):
  with members:
    - "learning concept hierarchies from text corpora using formal concept analysis"
    - "enriching very large ontologies using the www"

Connected through hyperedge HE-6 (induced by source paper "conscious machines and consciousness oriented programming"):
  with members:
    - "the socceral force"
    - "normalized information distance is not semicomputable"
    - "normalized web distance and word similarity"
    - "the similarity metric"

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.IR(Information Retrieval)