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 "learning representations of social media users"):
  Abstract: User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? #R##N#The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? #R##N#In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.

Includes members:
  Member "semeval 2017 task 4 sentiment analysis in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning")
  Member "multi task learning for argumentation mining in low resource settings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "semi supervised user geolocation via graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepgcns making gcns go as deep as cnns")
  Member "cascade contextual sarcasm detection in online discussion forums":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a transformer based approach to irony and sarcasm detection")
  Member "deep generalized canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "identifying beneficial task relations for multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bam born again multi task networks for natural language understanding")
  Member "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses")

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)