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: "on end to end program generation from user intention by deep neural networks"
  Abstract: This paper envisions an end-to-end program generation scenario using recurrent neural networks (RNNs): Users can express their intention in natural language; an RNN then automatically generates corresponding code in a characterby-by-character fashion. We demonstrate its feasibility through a case study and empirical analysis. To fully make such technique useful in practice, we also point out several cross-disciplinary challenges, including modeling user intention, providing datasets, improving model architectures, etc. Although much long-term research shall be addressed in this new field, we believe end-to-end program generation would become a reality in future decades, and we are looking forward to its practice.

Connected through hyperedge HE-1 (induced by source paper "type i attack for generative models"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "adversarial attacks on variational autoencoders"
    - "semantic adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "adversarial attack type i cheat classifiers by significant changes"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"

Connected through hyperedge HE-3 (induced by source paper "dataset for a neural natural language interface for databases nnlidb"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "learning a natural language interface with neural programmer"
    - "effective approaches to attention based neural machine translation"
    - "neural enquirer learning to query tables with natural language"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "deep api learning"):
  with members:
    - "parameter free probabilistic api mining across github"
    - "latent predictor networks for code generation"
    - "a convolutional attention network for extreme summarization of source code"
    - "swim synthesizing what i mean code search and idiomatic snippet synthesis"
    - "parameter free probabilistic api mining at github scale"
    - "convolutional neural networks over tree structures for programming language processing"
    - "learning natural coding conventions"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "latent predictor networks for code generation"):
  with members:
    - "exploring the limits of language modeling"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"
    - "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.SE(Software Engineering)