human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "feedforward sequential memory networks a new structure to learn long term dependency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving gated recurrent unit based acoustic modeling with batch normalization and enlarged context")
  Member "recurrent neural network regularization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "one billion word benchmark for measuring progress in statistical language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a fast and simple algorithm for training neural probabilistic language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "a bit of progress in language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling laws for neural language models")

Question: Please tell me which class the source paper 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)