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: "maximum margin bayesian networks"
  Abstract: We consider the problem of learning Bayesian network classifiers that maximize the marginover a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected graphical model need not respect. These additional constraints complicate the optimization task. Nevertheless, we derive an effective training algorithm that solves the maximum margin training problem for a range of Bayesian network topologies, and converges to an approximate solution for arbitrary network topologies. Experimental results show that the method can demonstrate improved generalization performance over Markov networks when the directed graphical structure encodes relevant knowledge. In practice, the training technique allows one to combine prior knowledge expressed as a directed (causal) model with state of the art discriminative learning methods.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust machine learning for real world systems"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-2 (induced by source paper "length bias in encoder decoder models and a case for global conditioning"):
  with members:
    - "globally normalized transition based neural networks"
    - "smart reply automated response suggestion for email"
    - "conversational contextual cues the case of personalization and history for response ranking"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "quantization based fast inner product search"
    - "sequence level training with recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "a neural conversational model"

Connected through hyperedge HE-3 (induced by source paper "learning discriminative bayesian networks from high dimensional continuous neuroimaging data"):
  with members:
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "learning gaussian networks"
    - "exact maximum margin structure learning of bayesian networks"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-4 (induced by source paper "exact maximum margin structure learning of bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "theory refinement on bayesian networks"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-5 (induced by source paper "large margin boltzmann machines and large margin sigmoid belief networks"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "learning arithmetic circuits"
    - "a new class of upper bounds on the log partition function"
    - "mixture of parents maximum entropy markov models"
    - "search based structured prediction"

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)