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: "lexis an optimization framework for discovering the hierarchical structure of sequential data"
  Abstract: Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as Lexis, that produces an optimized hierarchical representation of a given set of "target" strings. The resulting hierarchy, "Lexis-DAG", shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the "core" of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents.

Connected through hyperedge HE-1 (induced by source paper "semantic rl with action grammars data efficient learning of hierarchical task abstractions"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "the option critic architecture"
    - "towards deep symbolic reinforcement learning"
    - "adam a method for stochastic optimization"
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-2 (induced by source paper "evolution of hierarchical structure reuse in igem synthetic dna sequences"):
  with members:
    - "emergence and evolution of hierarchical structure in complex systems"
    - "the evolutionary origins of hierarchy"
    - "the hourglass effect in hierarchical dependency networks"
    - "evolution of a modular software network"

Connected through hyperedge HE-3 (induced by source paper "emergence and evolution of hierarchical structure in complex systems"):
  with members:
    - "the evolutionary origins of hierarchy"
    - "the hourglass effect in hierarchical dependency networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "the hourglass effect in hierarchical dependency networks"):
  with members:
    - "the evolutionary origins of hierarchy"
    - "core periphery structure in networks"
    - "evolution of a modular software network"

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)