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: "on horizontal and vertical separation in hierarchical text classification"
  Abstract: Hierarchy is an effective and common way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of "separable" models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer, but also in different layers.   Our main findings are the followings. First, we analyse the importance of separability on the data representation in the task of classification and based on that, we introduce "Strong Separation Principle" for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Significant Words Language Models (SWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real world data and demonstrate that how SWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure.

Connected through hyperedge HE-1 (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"
    - "parsimonious topic models with salient word discovery"
    - "integrating document clustering and topic modeling"

Connected through hyperedge HE-2 (induced by source paper "words are malleable computing semantic shifts in political and media discourse"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-3 (induced by source paper "hierarchical re estimation of topic models for measuring topical diversity"):
  with members:
    - "improving topic models with latent feature word representations"
    - "parsimonious topic models with salient word discovery"
    - "integrating document clustering and topic modeling"

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)