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 "dependencies formalising semantic catenae for information retrieval"):
  Abstract: Building machines that can understand text like humans is an AI-complete problem. A great deal of research has already gone into this, with astounding results, allowing everyday people to discuss with their telephones, or have their reading materials analysed and classified by computers. A prerequisite for processing text semantics, common to the above examples, is having some computational representation of text as an abstract object. Operations on this representation practically correspond to making semantic inferences, and by extension simulating understanding text. The complexity and granularity of semantic processing that can be realised is constrained by the mathematical and computational robustness, expressiveness, and rigour of the tools used. #R##N#This dissertation contributes a series of such tools, diverse in their mathematical formulation, but common in their application to model semantic inferences when machines process text. These tools are principally expressed in nine distinct models that capture aspects of semantic dependence in highly interpretable and non-complex ways. This dissertation further reflects on present and future problems with the current research paradigm in this area, and makes recommendations on how to overcome them. #R##N#The amalgamation of the body of work presented in this dissertation advances the complexity and granularity of semantic inferences that can be made automatically by machines.

Includes members:
  Member "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval":
    also connected through other hyperedges:
      (none)
  Member "preliminary experiments using subjective logic for the polyrepresentation of information needs":
    also connected through other hyperedges:
      (none)
  Member "rhetorical relations for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discourse in multimedia a case study in information extraction")
  Member "time series adaptive estimation of vaccination uptake using web search queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "web data mining for public health purposes")
  Member "part of speech based term weighting for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the estimation and use of statistical modelling in information retrieval")
  Member "learning to generate compositional color descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning of colors from color names distribution and point estimation")
  Member "adaptive distributional extensions to dfr ranking":
    also connected through other hyperedges:
      (none)
  Member "exploiting the bipartite structure of entity grids for document coherence and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weighted bipartite or directed stream graphs for the modeling of temporal networks")

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.IR(Information Retrieval)