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: "content based video indexing and retrieval using corr lda"
  Abstract: Existing video indexing and retrieval methods on popular web-based multimedia sharing websites are based on user-provided sparse tagging. This paper proposes a very specific way of searching for video clips, based on the content of the video. We present our work on Content-based Video Indexing and Retrieval using the Correspondence-Latent Dirichlet Allocation (corr-LDA) probabilistic framework. This is a model that provides for auto-annotation of videos in a database with textual descriptors, and brings the added benefit of utilizing the semantic relations between the content of the video and text. We use the concept-level matching provided by corr-LDA to build correspondences between text and multimedia, with the objective of retrieving content with increased accuracy. In our experiments, we employ only the audio components of the individual recordings and compare our results with an SVM-based approach.

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"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"

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"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "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-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"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "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-4 (induced by source paper "an unsupervised domain independent framework for automated detection of persuasion tactics in text"):
  with members:
    - "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"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "a heterogeneous graphical model to understand user level sentiments in social media"):
  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"
    - "user level sentiment analysis incorporating social networks"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (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"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "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-7 (induced by source paper "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"):
  with members:
    - "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"
    - "distributed representations of sentences and documents"
    - "why watching movie tweets won t tell the whole story"
    - "user level sentiment analysis incorporating social networks"

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.IR(Information Retrieval)