human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a heterogeneous graphical model to understand user level sentiments in social media"
  Abstract: Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for opinion mining and sentiment analysis. The detection and analysis of sentiment in social media is thus a valuable topic and attracts a lot of research efforts. Most of the earlier efforts focus on supervised learning approaches to solve this problem, which require expensive human annotations and therefore limits their practical use. In our work, we propose a semi-supervised approach to predict user-level sentiments for specific topics. We define and utilize a heterogeneous graph built from the social networks of the users with the knowledge that connected users in social networks typically share similar sentiments. Compared with the previous works, we have several novelties: (1) we incorporate the influences/authoritativeness of the users into the model, 2) we include comment-based and like-based user-user links to the graph, 3) we superimpose multiple heterogeneous graphs into one thereby allowing multiple types of links to exist between two users.

Connected through hyperedge HE-1 (induced by source paper "transition based dependency parsing using perceptron learner"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "modeling product search relevance in e commerce"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"

Connected through hyperedge HE-2 (induced by source paper "modeling product search relevance in e commerce"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"

Connected through hyperedge HE-3 (induced by source paper "a correspondence analysis framework for author conference recommendations"):
  with members:
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"

Connected through hyperedge HE-4 (induced by source paper "a machine learning framework for authorship identification from texts"):
  with members:
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"

Question: Please tell me which class the center node 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)