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: "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
  Abstract: We propose an approach to build a neural machine translation system with no supervised resources (i.e., no parallel corpora) using multimodal embedded representation over texts and images. Based on the assumption that text documents are often likely to be described with other multimedia information (e.g., images) somewhat related to the content, we try to indirectly estimate the relevance between two languages. Using multimedia as the "pivot", we project all modalities into one common hidden space where samples belonging to similar semantic concepts should come close to each other, whatever the observed space of each sample is. This modality-agnostic representation is the key to bridging the gap between different modalities. Putting a decoder on top of it, our network can flexibly draw the outputs from any input modality. Notably, in the testing phase, we need only source language texts as the input for translation. In experiments, we tested our method on two benchmarks to show that it can achieve reasonable translation performance. We compared and investigated several possible implementations and found that an end-to-end model that simultaneously optimized both rank loss in multimodal encoders and cross-entropy loss in decoders performed the best.

Connected through hyperedge HE-1 (induced by source paper "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"):
  with members:
    - "unsupervised multi modal neural machine translation"
    - "zero resource neural machine translation with multi agent communication game"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "phrase based neural unsupervised machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised neural machine translation"
    - "multi30k multilingual english german image descriptions"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "lessons learned in multilingual grounded language learning"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning language representations for typology prediction"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised multi modal neural machine translation"):
  with members:
    - "zero resource neural machine translation with multi agent communication game"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "unsupervised neural machine translation"
    - "inversenet solving inverse problems with splitting networks"

Connected through hyperedge HE-4 (induced by source paper "the graph based broad behavior aware recommendation system for interactive news"):
  with members:
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "very deep convolutional networks for end to end speech recognition"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "a probabilistic model to resolve diversity accuracy challenge of recommendation systems"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "emergent translation in multi agent communication"):
  with members:
    - "translating neuralese"
    - "imagination improves multimodal translation"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning visually grounded sentence representations"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "a correlational encoder decoder architecture for pivot based sequence generation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "multimodal pivots for image caption translation"
    - "variational neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised machine translation using monolingual corpora only"):
  with members:
    - "toward controlled generation of text"
    - "a teacher student framework for zero resource neural machine translation"
    - "style transfer from non parallel text by cross alignment"
    - "emergent translation in multi agent communication"
    - "unsupervised image to image translation networks"
    - "controllable invariance through adversarial feature learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "word translation without parallel data"

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)