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: "improving and scaling trans dimensional random field language models"
  Abstract: The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional random field (TRF) LM has been recently introduced to show superior performances, where the whole sentence is modeled as a random field. In this paper, we further develop the TDF LMs with two technical improvements, which are a new method of exploiting Hessian information in parameter optimization to further enhance the convergence of the training algorithm and an enabling method for training the TRF LMs on large corpus which may contain rare very long sentences. Experiments show that the TRF LMs can scale to using training data of up to 32 million words, consistently achieve 10% relative perplexity reductions over 5-gram LMs, and perform as good as NN LMs but with much faster speed in calculating sentence probabilities.

Connected through hyperedge HE-1 (induced by source paper "integrating discrete and neural features via mixed feature trans dimensional random field language models"):
  with members:
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "learning neural trans dimensional random field language models with noise contrastive estimation"
    - "language modeling with neural trans dimensional random fields"
    - "model interpolation with trans dimensional random field language models for speech recognition"
    - "efficient softmax approximation for gpus"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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.CL(Computation and Language)