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 "personalized word representations carrying personalized semantics learned from social network posts"):
  Abstract: Distributed word representations have been shown to be very useful in various natural language processing (NLP) application tasks. These word vectors learned from huge corpora very often carry both semantic and syntactic information of words. However, it is well known that each individual user has his own language patterns because of different factors such as interested topics, friend groups, social activities, wording habits, etc., which may imply some kind of personalized semantics. With such personalized semantics, the same word may imply slightly differently for different users. For example, the word "Cappuccino" may imply "Leisure", "Joy", "Excellent" for a user enjoying coffee, by only a kind of drink for someone else. Such personalized semantics of course cannot be carried by the standard universal word vectors trained with huge corpora produced by many people. In this paper, we propose a framework to train different personalized word vectors for different users based on the very successful continuous skip-gram model using the social network data posted by many individual users. In this framework, universal background word vectors are first learned from the background corpora, and then adapted by the personalized corpus for each individual user to learn the personalized word vectors. We use two application tasks to evaluate the quality of the personalized word vectors obtained in this way, the user prediction task and the sentence completion task. These personalized word vectors were shown to carry some personalized semantics and offer improved performance on these two evaluation tasks.

Includes members:
  Member "bag of tricks for efficient text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning")
  Member "finding function in form compositional character models for open vocabulary word representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "personalizing universal recurrent neural network language model with user characteristic features by social network crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gmail smart compose real time assisted writing")
  Member "document classification by inversion of distributed language representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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.CL(Computation and Language)