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: "structured message passing"
  Abstract: In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as algebraic decision diagrams and sparse hash tables. These representations can yield significant time and space savings over the conventional tabular representation when the message has several identical values (context-specific independence) or zeros (determinism) or both in its range. Therefore, in order to fully exploit the power of structured representations, we propose to artificially introduce context-specific independence and determinism in the messages. This yields a new class of powerful approximate inference algorithms which includes popular algorithms such as cluster-graph Belief propagation (BP), expectation propagation and particle BP as special cases. We show that our new algorithms introduce several interesting bias-variance trade-offs. We evaluate these trade-offs empirically and demonstrate that our new algorithms are more accurate and scalable than state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "faq questions asked frequently"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "join processing for graph patterns an old dog with new tricks"
    - "emptyheaded boolean algebra based graph processing"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-2 (induced by source paper "variable elimination in the fourier domain"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "a complete anytime algorithm for treewidth"
    - "on the partition function and random maximum a posteriori perturbations"
    - "tightening lp relaxations for map using message passing"
    - "join graph based cost shifting schemes"
    - "join graph propagation algorithms"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "oblivious bounds on the probability of boolean functions"):
  with members:
    - "dissociation and propagation for approximate lifted inference with standard relational database management systems"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "approximation by quantization"
    - "formula based probabilistic inference"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "optimal upper and lower bounds for boolean expressions by dissociation"
    - "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)