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: "peacock learning long tail topic features for industrial applications"
  Abstract: Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to $10^3$ topics, which cover difficultly the long-tail semantic word sets. In this paper, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a "big" LDA model with at least $10^5$ topics inferred from $10^9$ search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serving hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.

Connected through hyperedge HE-1 (induced by source paper "efficient correlated topic modeling with topic embedding"):
  with members:
    - "on unifying deep generative models"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "recurrent topic transition gan for visual paragraph generation"
    - "node2vec scalable feature learning for networks"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "nonparametric spherical topic modeling with word embeddings"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-2 (induced by source paper "diversity promoting bayesian learning of latent variable models"):
  with members:
    - "reducing overfitting in deep networks by decorrelating representations"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

Connected through hyperedge HE-3 (induced by source paper "fast online em for big topic modeling"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "learning topic models by belief propagation"
    - "online belief propagation for topic modeling"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "communication efficient parallel belief propagation for latent dirichlet allocation"
    - "on smoothing and inference for topic models"
    - "a new approach to speeding up topic modeling"
    - "a topic modeling toolbox using belief propagation"

Connected through hyperedge HE-4 (induced by source paper "scalable dynamic topic modeling with clustered latent dirichlet allocation clda"):
  with members:
    - "moliere automatic biomedical hypothesis generation system"
    - "bibliographic analysis with the citation network topic model"
    - "dependent hierarchical normalized random measures for dynamic topic modeling"
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"

Connected through hyperedge HE-5 (induced by source paper "streaming gibbs sampling for lda model"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"

Connected through hyperedge HE-6 (induced by source paper "latent variable modeling with diversity inducing mutual angular regularization"):
  with members:
    - "lshtc a benchmark for large scale text classification"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "probabilistic latent semantic analysis"
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "on the generalization error bounds of neural networks under diversity inducing mutual angular regularization"):
  with members:
    - "neural machine translation by jointly learning to align and translate"
    - "breaking the curse of dimensionality with convex neural networks"
    - "probabilistic latent semantic analysis"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "zenlda an efficient and scalable topic model training system on distributed data parallel platform"):
  with members:
    - "mllib machine learning in apache spark"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "probabilistic latent semantic analysis"

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)