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 modeling using latent permutations"
  Abstract: We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.

Connected through hyperedge HE-1 (induced by source paper "optimal learning of mallows block model"):
  with members:
    - "efficiently learning mixtures of mallows models"
    - "learning mixtures of ranking models"
    - "consensus ranking under the exponential model"
    - "statistical exponential families a digest with flash cards"
    - "sorting from noisy information"

Connected through hyperedge HE-2 (induced by source paper "boosting entity linking performance by leveraging unlabeled documents"):
  with members:
    - "improving entity linking by modeling latent relations between mentions"
    - "deep joint entity disambiguation with local neural attention"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "distant supervision for entity linking"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning and segmentation of complex activities from video"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "efficient unsupervised temporal segmentation of motion data"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised learning of video representations using lstms"
    - "an end to end generative framework for video segmentation and recognition"
    - "unsupervised learning from narrated instruction videos"
    - "action recognition using visual attention"

Connected through hyperedge HE-4 (induced by source paper "sort story sorting jumbled images and captions into stories"):
  with members:
    - "learning visual storylines with skipping recurrent neural networks"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "skip thought vectors"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "learning temporal embeddings for complex video analysis"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "order embeddings of images and language"

Connected through hyperedge HE-5 (induced by source paper "jointly modeling topics and intents with global order structure"):
  with members:
    - "bayesian inference with posterior regularization and applications to infinite latent svms"
    - "catching the drift probabilistic content models with applications to generation and summarization"

Connected through hyperedge HE-6 (induced by source paper "coarse grained cross lingual alignment of comparable texts with topic models and encyclopedic knowledge"):
  with members:
    - "multilingual topic models for unaligned text"

Connected through hyperedge HE-7 (induced by source paper "generating natural language descriptions from owl ontologies the natural owl system"):
  with members:
    - "catching the drift probabilistic content models with applications to generation and summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "trainable methods for surface natural language generation"
    - "a flexible shallow approach to text generation"

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)