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: "a correlational encoder decoder architecture for pivot based sequence generation"
  Abstract: Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.

Connected through hyperedge HE-1 (induced by source paper "unsupervised neural machine translation with weight sharing"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "multi channel encoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-2 (induced by source paper "imagination improves multimodal translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "multi task video captioning with video and entailment generation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multimodal pivots for image caption translation"
    - "variational neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "learning joint multilingual sentence representations with neural machine translation"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "billion scale similarity search with gpus"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "trans gram fast cross lingual word embeddings"
    - "multi source neural translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-4 (induced by source paper "common representation learning using step based correlation multi modal cnn"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "linking image and text with 2 way nets"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "correlational neural networks"
    - "nonparametric canonical correlation analysis"

Connected through hyperedge HE-5 (induced by source paper "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"):
  with members:
    - "zero resource translation with multi lingual neural machine translation"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "densecap fully convolutional localization networks for dense captioning"
    - "minimum risk training for neural machine translation"
    - "multilingual image description with neural sequence models"
    - "bridge correlational neural networks for multilingual multimodal representation learning"
    - "multi task sequence to sequence learning"

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)