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: "reduced ordered binary decision diagram with implied literals a new knowledge compilation approach"
  Abstract: Reduced ordered binary decision diagram (ROBDD) is one of the most influential knowledge compilation languages. We generalize it by associating some implied literals with each node to propose a new language called ROBDD with implied literals (ROBDD-#N#                  #N#                    #N#                  #N#                  $$L$$#N#                ) and show that ROBDD-#N#                  #N#                    #N#                  #N#                  $$L$$#N#                 can meet most of the querying requirements involved in the knowledge compilation map. Then, we discuss a kind of subsets of ROBDD-#N#                  #N#                    #N#                  #N#                  $$L$$#N#                 called ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_i$$#N#                 with precisely #N#                  #N#                    #N#                  #N#                  $$i$$#N#                 implied literals #N#                  #N#                    #N#                  #N#                  $$(0\le i\le \infty )$$#N#                , where ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_0$$#N#                 is isomorphic to ROBDD. ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_i$$#N#                 has uniqueness over any given linear order of variables. We mainly focus on ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 and demonstrate that: (a) it is a canonical representation on any given variable order; (b) it is the most succinct subset in ROBDD-#N#                  #N#                    #N#                  #N#                  $$L$$#N#                 and thus also meets most of the querying requirements; (c) given any logical operation ROBDD supports in polytime, ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 can also support it in time polynomial in the sizes of the equivalent ROBDDs. Moreover, we propose an ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_i$$#N#                 compilation algorithm for any #N#                  #N#                    #N#                  #N#                  $$i$$#N#                 and an ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 compilation algorithm, and then we implement an ROBDD-#N#                  #N#                    #N#                  #N#                  $$L$$#N#                 package called BDDjLu. Our preliminary experimental results indicate that: (a) the compilation results of ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 are significantly smaller than those of ROBDD; (b) the standard d-DNNF compiler c2d and our ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 compiler do not dominate the other, yet ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 has canonicity and supports more querying requirements and relatively efficient logical operations; and (c) the method that first compiles knowledge base into ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 and then converts ROBDD-#N#                  #N#                    #N#                  #N#                  $$L_\infty $$#N#                 into ROBDD provides an efficient ROBDD compiler.

Connected through hyperedge HE-1 (induced by source paper "approximate model counting by partial knowledge compilation"):
  with members:
    - "uniform solution sampling using a constraint solver as an oracle"
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "the language of search"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (induced by source paper "augmenting ordered binary decision diagrams with conjunctive decomposition"):
  with members:
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "a knowledge compilation map"

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)