human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "p model alternative to the t model"):
  Abstract: Standard linguistic analysis of syntax uses the T-model.   This model #N#requires the ordering:  D-structure $>$ S-structure $>$ LF,   #N#where D-structure is the deep structure,  #N#S-structure is the surface structure,  and LF is logical form.   #N#Between each of these representations there is movement which alters #N#the order of the constituent words;  movement is achieved using the principles#N#and parameters of syntactic theory.   Psychological analysis of sentence #N#production is usually either serial or connectionist.   Psychological serial#N#models do not accommodate the T-model immediately so that here a new model #N#called the P-model is introduced.   The P-model is different from previous #N#linguistic and psychological models.   Here it is argued that the LF #N#representation should be replaced by a variant #N#of Frege's three qualities (sense,  reference,  and force),  #N#called the Frege representation or F-representation.#N#In the F-representation the order of elements is not necessarily the same as #N#that in LF and it is suggested that the correct ordering is: #N#F-representation $>$ D-structure $>$ S-structure.   #N#This ordering appears to lead to a more natural #N#view of sentence production and processing.   Within this framework movement #N#originates as the outcome of emphasis applied to the sentence.   The #N#requirement that the F-representation precedes the D-structure needs a picture#N#of the particular principles and parameters which pertain to movement of words#N#between representations.   In general this would imply that there is a #N#preferred or optimal ordering of the symbolic string in the F-representation.#N#The standard ordering is retained because the general way of producing #N#such an optimal ordering is unclear.   In this case it is possible to produce #N#an analysis of movement between LF and D-structure similar to the usual #N#analysis of movement between S-structure and LF.   #N#It is suggested that a maximal amount of information about #N#a language's grammar and lexicon is stored,    #N#because of the necessity of analyzing corrupted data.

Includes members:
  Member "a classification approach to word prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "state of the art evaluation and recommendations regarding document processing and visualization techniques")
  Member "an ascription based approach to speech acts":
    also connected through other hyperedges:
      (none)
  Member "cascaded markov models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based shallow parsing")
  Member "learning transformation rules to find grammatical relations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using existing systems to supplement small amounts of annotated grammatical relations training data")
  Member "ultrametric distance in syntax":
    also connected through other hyperedges:
      (none)
  Member "does meaning evolve":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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.CL(Computation and Language)