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: "unsupervised sentence representations as word information series revisiting tf idf"
  Abstract: Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency--Inverse Document Frequency (TF--IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.

Connected through hyperedge HE-1 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-2 (induced by source paper "sentence embeddings for russian nlu"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "catboost unbiased boosting with categorical features"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "on the possibility of rewarding structure learning agents mutual information on linguistic random sets"):
  with members:
    - "miss tools and mr fruit emergent communication in agents learning about object affordances"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "a survey of reinforcement learning informed by natural language"
    - "diversity is all you need learning skills without a reward function"
    - "mihash online hashing with mutual information"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)