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: "an efficient message passing algorithm for the m best map problem"
  Abstract: Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model known as the maximum a posteriori (MAP) inference problem. In many situations, one could benefit from having not just a single solution, but the top M most probable solutions known as the M-Best MAP problem. In this paper, we propose an efficient message-passing based algorithm for solving the M-Best MAP problem. Specifically, our algorithm solves the recently proposed Linear Programming (LP) formulation of M-Best MAP [7], while being orders of magnitude faster than a generic LP-solver. Our approach relies on studying a particular partial Lagrangian relaxation of the M-Best MAP LP which exposes a natural combinatorial structure of the problem that we exploit.

Connected through hyperedge HE-1 (induced by source paper "bidirectional beam search forward backward inference in neural sequence models for fill in the blank image captioning"):
  with members:
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "image captioning with deep bidirectional lstms"
    - "exploring models and data for image question answering"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "vqa visual question answering"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "incremental tube construction for human action detection"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "action tubelet detector for spatio temporal action localization"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "ssd single shot multibox detector"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "actionness estimation using hybrid fully convolutional networks"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "diverse m best solutions by dynamic programming"):
  with members:
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"

Connected through hyperedge HE-4 (induced by source paper "herding generalizes diverse m best solutions"):
  with members:
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "super samples from kernel herding"

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)