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: "context mover s distance barycenters optimal transport of contexts for building representations"
  Abstract: We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider representation learning from the perspective of Optimal Transport and take advantage of its tools such as Wasserstein distance and barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance (quantitatively as well as qualitatively) on tasks such as measuring sentence similarity, word entailment and similarity, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec, GenSen).  The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. The code, as well as prebuilt histograms, are available under https://github.com/context-mover/.

Connected through hyperedge HE-1 (induced by source paper "structural aware sentence similarity with recursive optimal transport"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "learning semantic textual similarity from conversations"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "parameter free sentence embedding via orthogonal basis"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-2 (induced by source paper "learning embeddings into entropic wasserstein spaces"):
  with members:
    - "stochastic wasserstein barycenters"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "deep contextualized word representations"
    - "imagenet pre trained models with batch normalization"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "learning with a wasserstein loss"
    - "order embeddings of images and language"

Connected through hyperedge HE-3 (induced by source paper "gloss generative latent optimization of sentence representations"):
  with members:
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "learning distributed representations of sentences from unlabelled data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "model fusion via optimal transport"):
  with members:
    - "federated learning with matched averaging"
    - "wasserstein barycenter model ensembling"
    - "how important is a neuron"
    - "non iterative knowledge fusion in deep convolutional neural networks"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "axiomatic attribution for deep networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "poincar e glove hyperbolic word embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "hierarchical density order embeddings"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

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)