human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "teals time aware text embedding approach to leverage subgraphs"):
  Abstract: Given a graph over which the contagions (e.g. virus, gossip) propagate, leveraging subgraphs with highly correlated nodes is beneficial to many applications. Yet, challenges abound. First, the propagation pattern between a pair of nodes may change in various temporal dimensions. Second, not always the same contagion is propagated. Hence, state-of-the-art text mining approaches ranging from similarity measures to topic-modeling cannot use the textual contents to compute the weights between the nodes. Third, the word-word co-occurrence patterns may differ in various temporal dimensions, which increases the difficulty to employ current word embedding approaches. We argue that inseparable multi-aspect temporal collaborations are inevitably needed to better calculate the correlation metrics in dynamical processes. In this work, we showcase a sophisticated framework that on the one hand, integrates a neural network based time-aware word embedding component that can collectively construct the word vectors through an assembly of infinite latent temporal facets, and on the other hand, uses an elaborate generative model to compute the edge weights through heterogeneous temporal attributes. After computing the intra-nodes weights, we utilize our Max-Heap Graph cutting algorithm to exploit subgraphs. We then validate our model through comprehensive experiments on real-world propagation data. The results show that the knowledge gained from the versatile temporal dynamics is not only indispensable for word embedding approaches but also plays a significant role in the understanding of the propagation behaviors. Finally, we demonstrate that compared with other rivals, our model can dominantly exploit the subgraphs with highly coordinated nodes.

Includes members:
  Member "improving topic models with latent feature word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "learning word relatedness over time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soulmate short text author linking through multi aspect temporal textual embedding")
  Member "approximation algorithms for reducing the spectral radius to control epidemic spread":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)