human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "using text embeddings for causal inference"):
  Abstract: We address causal inference with text documents. For example, does adding a theorem to a paper affect its chance of acceptance? Does reporting the gender of a forum post author affect the popularity of the post? We estimate these effects from observational data, where they may be confounded by features of the text such as the subject or writing quality. Although the text suffices for causal adjustment, it is prohibitively high-dimensional. The challenge is to find a low-dimensional text representation that can be used in causal inference. A key insight is that causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome. Our proposed method adapts deep language models to learn low-dimensional embeddings from text that predict these values well; these embeddings suffice for causal adjustment. We establish theoretical properties of this method. We study it empirically on semi-simulated and real data on paper acceptance and forum post popularity. Code is available at this https URL.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "challenges of using text classifiers for causal inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking text attribute transfer a lexical analysis")
  Member "a dataset of peer reviews peerread collection insights and nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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.LG(Machine Learning)