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: "pca 4 dca the application of principal component analysis to the dendritic cell algorithm"
  Abstract: As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is#R##N#based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by#R##N#the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this#R##N#paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress#R##N#recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

Connected through hyperedge HE-1 (induced by source paper "the dendritic cell algorithm for intrusion detection"):
  with members:
    - "integrating real time analysis with the dendritic cell algorithm through segmentation"
    - "further exploration of the dendritic cell algorithm antigen multiplier and time windows"
    - "introducing dendritic cells as a novel immune inspired algorithm for anomoly detection"
    - "exploration of the dendritic cell algorithm using the duration calculus"
    - "the application of a dendritic cell algorithm to a robotic classifier"
    - "articulation and clarification of the dendritic cell algorithm"
    - "dca for bot detection"
    - "immune system approaches to intrusion detection a review"

Connected through hyperedge HE-2 (induced by source paper "theoretical formulation and analysis of the deterministic dendritic cell algorithm"):
  with members:
    - "the deterministic dendritic cell algorithm"
    - "integrating real time analysis with the dendritic cell algorithm through segmentation"
    - "further exploration of the dendritic cell algorithm antigen multiplier and time windows"
    - "exploration of the dendritic cell algorithm using the duration calculus"
    - "the application of a dendritic cell algorithm to a robotic classifier"
    - "dca for bot detection"

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)