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: "lessons learned in multilingual grounded language learning"
  Abstract: Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.

Connected through hyperedge HE-1 (induced by source paper "countering language drift via visual grounding"):
  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"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-2 (induced by source paper "multi head attention with diversity for learning grounded multilingual multimodal representations"):
  with members:
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "imagination improves multimodal translation"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "bootstrapping disjoint datasets for multilingual multimodal representation learning"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "zero resource neural machine translation with multi agent communication game"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "limitations of cross lingual learning from image search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "emergent translation in multi agent communication"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-4 (induced by source paper "cross lingual visual verb sense disambiguation"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "sockeye a toolkit for neural machine translation"
    - "cuni system for the wmt17 multimodal translation task"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "image pivoting for learning multilingual multimodal representations"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"

Connected through hyperedge HE-5 (induced by source paper "symbolic inductive bias for visually grounded learning of spoken language"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "the natural language decathlon multitask learning as question answering"
    - "syntactic scaffolds for semantic structures"
    - "sequence based multi lingual low resource speech recognition"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "scheduled multi task learning from syntax to translation"
    - "visually grounded learning of keyword prediction from untranscribed speech"

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)