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 "towards semantic query segmentation"):
  Abstract: Query Segmentation is one of the critical components for understanding users' search intent in Information Retrieval tasks. It involves grouping tokens in the search query into meaningful phrases which help downstream tasks like search relevance and query understanding. In this paper, we propose a novel approach to segment user queries using distributed query embeddings. Our key contribution is a supervised approach to the segmentation task using low-dimensional feature vectors for queries, getting rid of traditional hand tuned and heuristic NLP features which are quite expensive. #R##N#We benchmark on a 50,000 human-annotated web search engine query corpus achieving comparable accuracy to state-of-the-art techniques. The advantage of our technique is its fast and does not use external knowledge-base like Wikipedia for score boosting. This helps us generalize our approach to other domains like eCommerce without any fine-tuning. We demonstrate the effectiveness of this method on another 50,000 human-annotated eCommerce query corpus from eBay search logs. Our approach is easy to implement and generalizes well across different search domains proving the power of low-dimensional embeddings in query segmentation task, opening up a new direction of research for this problem.

Includes members:
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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.IR(Information Retrieval)