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: "improved dynamic schedules for belief propagation"
  Abstract: Belief propagation and its variants are popular methods for approximate inference, but their running time and even their convergence depend greatly on the schedule used to send the messages. Recently, dynamic update schedules have been shown to converge much faster on hard networks than static schedules, namely the residual BP schedule of Elidan et al. [2006]. But that RBP algorithm wastes message updates: many messages are computed solely to determine their priority, and are never actually performed. In this paper, we show that estimating the residual, rather than calculating it directly, leads to significant decreases in the number of messages required for convergence, and in the total running time. The residual is estimated using an upper bound based on recent work on message errors in BP. On both synthetic and real-world networks, this dramatically decreases the running time of BP, in some cases by a factor of five, without affecting the quality of the solution.

Connected through hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "the lock free k lsm relaxed priority queue"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-2 (induced by source paper "graph partition neural networks for semi supervised classification"):
  with members:
    - "situation recognition with graph neural networks"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "graph structured representations for visual question answering"
    - "the more you know using knowledge graphs for image classification"
    - "learning multiagent communication with backpropagation"

Connected through hyperedge HE-3 (induced by source paper "neural factor graph models for cross lingual morphological tagging"):
  with members:
    - "morphological segmentation inside out"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "cross lingual morphological tagging for low resource languages"
    - "neural morphological tagging from characters for morphologically rich languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "join graph based cost shifting schemes"):
  with members:
    - "a complete anytime algorithm for treewidth"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "tightening lp relaxations for map using message passing"
    - "join graph propagation algorithms"
    - "lagrangian relaxation for map estimation in graphical models"
    - "arc consistency for soft constraints"

Connected through hyperedge HE-5 (induced by source paper "interpreting graph cuts as a max product algorithm"):
  with members:
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "on the optimality of tree reweighted max product message passing"

Connected through hyperedge HE-6 (induced by source paper "message error analysis of loopy belief propagation for the sum product algorithm"):
  with members:
    - "accuracy bounds for belief propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "loopy belief propogation and gibbs measures"
    - "graph zeta function in the bethe free energy and loopy belief propagation"
    - "sufficient conditions for convergence of the sum product algorithm"

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.LG(Machine Learning)