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: "nationality classification using name embeddings"
  Abstract: Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification. #R##N#We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available. #R##N#As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.

Connected through hyperedge HE-1 (induced by source paper "single training dimension selection for word embedding with pca"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "simple and effective dimensionality reduction for word embeddings"
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "poincar e embeddings for learning hierarchical representations"
    - "swivel improving embeddings by noticing what s missing"

Connected through hyperedge HE-2 (induced by source paper "the secret lives of names name embeddings from social media"):
  with members:
    - "the preeminence of ethnic diversity in scientific collaboration"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "examining scientific writing styles from the perspective of linguistic complexity"):
  with members:
    - "on the similarities between native non native and translated texts"
    - "coauthorship networks a directed network approach considering the order and number of coauthors"
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-4 (induced by source paper "the preeminence of ethnic diversity in scientific collaboration"):
  with members:
    - "quantifying long term scientific impact"

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.SI(Social and Information Networks)