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 "low dimensional query projection based on divergence minimization feedback model for ad hoc retrieval"):
  Abstract: Low-dimensional word vectors have long been used in a wide range of applications in natural language processing. In this paper we shed light on estimating query vectors in ad-hoc retrieval where a limited information is available in the original query. Pseudo-relevance feedback (PRF) is a well-known technique for updating query language models and expanding the queries with a number of relevant terms. We formulate the query updating in low-dimensional spaces first with rotating the query vector and then with scaling. These consequential steps are embedded in a query-specific projection matrix capturing both angle and scaling. In this paper we propose a new but not the most effective technique necessarily for PRF in language modeling, based on the query projection algorithm. We learn an embedded coefficient matrix for each query, whose aim is to improve the vector representation of the query by transforming it to a more reliable space, and then update the query language model. The proposed embedded coefficient divergence minimization model (ECDMM) takes top-ranked documents retrieved by the query and obtains a couple of positive and negative sample sets; these samples are used for learning the coefficient matrix which will be used for projecting the query vector and updating the query language model using a softmax function. Experimental results on several TREC and CLEF data sets in several languages demonstrate effectiveness of ECDMM. The experimental results reveal that the new formulation for the query works as well as state-of-the-art PRF techniques and outperforms state-of-the-art PRF techniques in a TREC collection in terms of MAP,P@5, and P@10 significantly.

Includes members:
  Member "dimension projection among languages based on pseudo relevant documents for query translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ss4mct a statistical stemmer for morphologically complex texts")
  Member "a multiplicative model for learning distributed text based attribute representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "word2vec explained deriving mikolov et al s negative sampling word embedding method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "natural language processing almost from scratch":
    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.IR(Information Retrieval)