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: "any space probabilistic inference"
  Abstract: We have recently introduced an any-space algorithm for exact inference in Bayesian networks, called Recursive Conditioning, RC, which allows one to trade space with time at increments of X-bytes, where X is the number of bytes needed to cache a floating point number. In this paper, we present three key extensions of RC. First, we modify the algorithm so it applies to more general factorization of probability distributions, including (but not limited to) Bayesian network factorizations. Second, we present a forgetting mechanism which reduces the space requirements of RC considerably and then compare such requirmenets with those of variable elimination on a number of realistic networks, showing orders of magnitude improvements in certain cases. Third, we present a version of RC for computing maximum a posteriori hypotheses (MAP), which turns out to be the first MAP algorithm allowing a smooth time-space tradeoff. A key advantage of presented MAP algorithm is that it does not have to start from scratch each time a new query is presented, but can reuse some of its computations across multiple queries, leading to significant savings in ceratain cases.

Connected through hyperedge HE-1 (induced by source paper "approximating map using local search"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "exploiting causal independence in bayesian network inference"

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)