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: "first experiments with neural translation of informal to formal mathematics"
  Abstract: We report on our experiments to train deep neural networks that automatically translate informalized LaTeX-written Mizar texts into the formal Mizar language. To the best of our knowledge, this is the first time when neural networks have been adopted in the formalization of mathematics. Using Luong et al.'s neural machine translation model (NMT), we tested our aligned informal-formal corpora against various hyperparameters and evaluated their results. Our experiments show that our best performing model configurations are able to generate correct Mizar statements on 65.73\% of the inference data, with the union of all models covering 79.17\%. These results indicate that formalization through artificial neural network is a promising approach for automated formalization of mathematics. We present several case studies to illustrate our results.

Connected through hyperedge HE-1 (induced by source paper "exploration of neural machine translation in autoformalization of mathematics in mizar"):
  with members:
    - "guiding theorem proving by recurrent neural networks"
    - "cross lingual language model pretraining"
    - "gradient descent finds global minima of deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "visual object networks image generation with disentangled 3d representation"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"

Connected through hyperedge HE-3 (induced by source paper "pix2vex image to geometry reconstruction using a smooth differentiable renderer"):
  with members:
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "live intrinsic material estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "inverse transport networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-4 (induced by source paper "guiding theorem proving by recurrent neural networks"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "gamepad a learning environment for theorem proving"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "beam search strategies for neural machine translation"
    - "enigma efficient learning based inference guiding machine"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning in hol4"):
  with members:
    - "hammering mizar by learning clause guidance"
    - "learning a sat solver from single bit supervision"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "tactictoe learning to reason with hol4 tactics"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "can neural networks learn symbolic rewriting"):
  with members:
    - "tree2tree neural translation model for learning source code changes"
    - "can neural networks understand logical entailment"
    - "attention is all you need"
    - "developing corpus based translation methods between informal and formal mathematics project description"

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)