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: "and or multi valued decision diagrams aomdds for graphical models"
  Abstract: Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.

Connected through hyperedge HE-1 (induced by source paper "compiling stochastic constraint programs to and or decision diagrams"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "spudd stochastic planning using decision diagrams"
    - "stochastic constraint programming"
    - "solving sat and bayesian inference with backtracking search"
    - "stochastic constraint programming a scenario based approach"

Connected through hyperedge HE-2 (induced by source paper "approximate model counting by partial knowledge compilation"):
  with members:
    - "reduced ordered binary decision diagram with implied literals a new knowledge compilation approach"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "the language of search"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "weighted positive binary decision diagrams for exact probabilistic inference"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "nonuniform dynamic discretization in hybrid networks"
    - "context specific independence in bayesian networks"
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "sum product networks a new deep architecture"
    - "counting belief propagation"

Connected through hyperedge HE-4 (induced by source paper "augmenting ordered binary decision diagrams with conjunctive decomposition"):
  with members:
    - "reduced ordered binary decision diagram with implied literals a new knowledge compilation approach"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "approximation by quantization"):
  with members:
    - "mini bucket heuristics for improved search"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "expectation propagation for approximate bayesian inference"
    - "context specific independence in bayesian networks"
    - "formula based probabilistic inference"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "join graph propagation algorithms"

Connected through hyperedge HE-6 (induced by source paper "algebraic model counting"):
  with members:
    - "symmetric weighted first order model counting"
    - "skolemization for weighted first order model counting"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "solving sat and bayesian inference with backtracking search"
    - "a knowledge compilation map"

Connected through hyperedge HE-7 (induced by source paper "ordered and or decomposition and binary decision diagram"):
  with members:
    - "the language of search"
    - "a knowledge compilation map"

Connected through hyperedge HE-8 (induced by source paper "algorithms for generating ordered solutions for explicit and or structures"):
  with members:
    - "anytime heuristic search"

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)