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: "concept trees building dynamic concepts from semi structured data using nature inspired methods"
  Abstract: This paper describes a method for creating structure from heterogeneous sources, as part of an information database, or more specifically, a 'concept base'. Structures called 'concept trees' can grow from the semi-structured sources when consistent sequences of concepts are presented. They might be considered to be dynamic databases, possibly a variation on the distributed Agent-Based or Cellular Automata models, or even related to Markov models. Semantic comparison of text is required, but the trees can be built more, from automatic knowledge and statistical feedback. This reduced model might also be attractive for security or privacy reasons, as not all of the potential data gets saved. The construction process maintains the key requirement of generality, allowing it to be used as part of a generic framework. The nature of the method also means that some level of optimisation or normalisation of the information will occur. This gives comparisons with databases or knowledge-bases, but a database system would firstly model its environment or datasets and then populate the database with instance values. The concept base deals with a more uncertain environment and therefore cannot fully model it beforehand. The model itself therefore evolves over time. Similar to databases, it also needs a good indexing system, where the construction process provides memory and indexing structures. These allow for more complex concepts to be automatically created, stored and retrieved, possibly as part of a more cognitive model. There are also some arguments, or more abstract ideas, for merging physical-world laws into these automatic processes.

Connected through hyperedge HE-1 (induced by source paper "a brain like cognitive process with shared methods"):
  with members:
    - "one shot adaptation of supervised deep convolutional models"
    - "turing then now and still key"

Connected through hyperedge HE-2 (induced by source paper "new ideas for brain modelling 4"):
  with members:
    - "a metric for modelling and measuring complex behavioural systems"
    - "tree pruning for new search techniques in computer games"
    - "turing then now and still key"

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)