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: "integrating document clustering and topic modeling"
  Abstract: Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.

Connected through hyperedge HE-1 (induced by source paper "a new evaluation framework for topic modeling algorithms based on synthetic corpora"):
  with members:
    - "provable algorithms for inference in topic models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-2 (induced by source paper "clustering prominent people and organizations in topic specific text corpora"):
  with members:
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "named entity recognition with bidirectional lstm cnns"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-3 (induced by source paper "hitr hierarchical topic model re estimation for measuring topical diversity of documents"):
  with members:
    - "improving topic models with latent feature word representations"
    - "hierarchical re estimation of topic models for measuring topical diversity"
    - "on horizontal and vertical separation in hierarchical text classification"
    - "parsimonious topic models with salient word discovery"

Connected through hyperedge HE-4 (induced by source paper "topic representation finding more representative words in topic models"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "word storms multiples of word clouds for visual comparison of documents"

Connected through hyperedge HE-5 (induced by source paper "improving topic models with latent feature word representations"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "learning structured text representations"):
  with members:
    - "structured attention networks"
    - "frustratingly short attention spans in neural language modeling"
    - "neural discourse structure for text categorization"
    - "cross lingual rst discourse parsing"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-7 (induced by source paper "hierarchical re estimation of topic models for measuring topical diversity"):
  with members:
    - "improving topic models with latent feature word representations"
    - "on horizontal and vertical separation in hierarchical text classification"
    - "parsimonious topic models with salient word discovery"

Connected through hyperedge HE-8 (induced by source paper "query focused opinion summarization for user generated content"):
  with members:
    - "large margin learning of submodular summarization methods"
    - "bayesian query focused summarization"

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)