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 "sector a neural model for coherent topic segmentation and classification"):
  Abstract: When searching for information, a human reader first glances over a document, spots relevant sections and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates to identify the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available dataset with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 compared to state-of-the-art CNN classifiers with baseline segmentation.

Includes members:
  Member "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval")
  Member "characterizing question facets for complex answer retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overcoming low utility facets for complex answer retrieval")
  Member "structuring wikipedia articles with section recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying engagement with citations on wikipedia")
  Member "text segmentation as a supervised learning task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation")
  Member "do convolutional networks need to be deep for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "word class embeddings for multiclass text classification")
  Member "benchmark for complex answer retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "getting deep recommenders fit bloom embeddings for sparse binary input output networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "advances in collaborative filtering and ranking")
  Member "wikireading a novel large scale language understanding task over wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "kleister a novel task for information extraction involving long documents with complex layout")

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.CL(Computation and Language)