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: "entropy based pruning of backoff language models"
  Abstract: A criterion for pruning parameters from N-gram backoff language models is developed, based on the relative entropy between the original and the pruned model. It is shown that the relative entropy resulting from pruning a single N-gram can be computed exactly and efficiently for backoff models. The relative entropy measure can be expressed as a relative change in training set perplexity. This leads to a simple pruning criterion whereby all N-grams that change perplexity by less than a threshold are removed from the model. Experiments show that a production-quality Hub4 LM can be reduced to 26% its original size without increasing recognition error. We also compare the approach to a heuristic pruning criterion by Seymore and Rosenfeld (1996), and show that their approach can be interpreted as an approximation to the relative entropy criterion. Experimentally, both approaches select similar sets of N-grams (about 85% overlap), with the exact relative entropy criterion giving marginally better performance.

Connected through hyperedge HE-1 (induced by source paper "approximating probabilistic models as weighted finite automata"):
  with members:
    - "weighted automata extraction from recurrent neural networks via regression on state spaces"
    - "federated learning of n gram language models"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "mobile keyboard input decoding with finite state transducers"

Connected through hyperedge HE-2 (induced by source paper "investigation on n gram approximated rnnlms for recognition of morphologically rich speech"):
  with members:
    - "n gram language modeling using recurrent neural network estimation"
    - "automatic speech recognition with very large conversational finnish and estonian vocabularies"
    - "recurrent neural network regularization"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-3 (induced by source paper "projectionnet learning efficient on device deep networks using neural projections"):
  with members:
    - "neural graph machines learning neural networks using graphs"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "smart reply automated response suggestion for email"
    - "revisiting semi supervised learning with graph embeddings"
    - "learning to hash for indexing big data a survey"
    - "compressing neural networks with the hashing trick"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "hash embeddings for efficient word representations"):
  with members:
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for natural language processing"
    - "neural machine translation with characters and hierarchical encoding"
    - "hash2vec feature hashing for word embeddings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "scalable machine translation in memory constrained environments"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "compositional morphology for word representations and language modelling"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "pragmatic neural language modelling in machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-6 (induced by source paper "the ibm 2016 english conversational telephone speech recognition system"):
  with members:
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "very deep convolutional networks for large scale image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "connecting phrase based statistical machine translation adaptation"):
  with members:
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-8 (induced by source paper "finding function in form compositional character models for open vocabulary word representation"):
  with members:
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "compositional morphology for word representations and language modelling"
    - "structure regularization for structured prediction theories and experiments"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)