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: "pertinent information retrieval based on possibilistic bayesian network origin and possibilistic perspective"
  Abstract: In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a g eneral Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears bec ause of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To r esolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a h ybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzzin ess of natural language. We believe that the translation of Bayesi an network model from the probabilistic framework to possibili stic one will allow a performance improvement of BNRM.

Connected through hyperedge HE-1 (induced by source paper "mouse movement and probabilistic graphical models based e learning activity recognition improvement possibilistic model"):
  with members:
    - "hierarchical hidden markov model in detecting activities of daily living in wearable videos for studies of dementia"
    - "hidden markov model for inferring learner task using mouse movement"
    - "fuzzy knowledge representation learning and optimization with bayesian analysis in fuzzy semantic networks"

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)