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 "information extraction from scientific literature for method recommendation"):
  Abstract: As a research community grows, more and more papers are published each year. As a result there is increasing demand for improved methods for finding relevant papers, automatically understanding the key ideas and recommending potential methods for a target problem. Despite advances in search engines, it is still hard to identify new technologies according to a researcher's need. Due to the large variety of domains and extremely limited annotated resources, there has been relatively little work on leveraging natural language processing in scientific recommendation. In this proposal, we aim at making scientific recommendations by extracting scientific terms from a large collection of scientific papers and organizing the terms into a knowledge graph. In preliminary work, we trained a scientific term extractor using a small amount of annotated data and obtained state-of-the-art performance by leveraging large amount of unannotated papers through applying multiple semi-supervised approaches. We propose to construct a knowledge graph in a way that can make minimal use of hand annotated data, using only the extracted terms, unsupervised relational signals such as co-occurrence, and structural external resources such as Wikipedia. Latent relations between scientific terms can be learned from the graph. Recommendations will be made through graph inference for both observed and unobserved relational pairs.

Includes members:
  Member "multi task identification of entities relations and coreference for scientific knowledge graph construction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "knowledge graph completion via complex tensor factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inductive relation prediction by subgraph reasoning")
  Member "graph based semi supervised conditional random fields for spoken language understanding using unaligned data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asking clarifying questions in open domain information seeking conversations")
  Member "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain independent extraction of scientific concepts from research articles")
  Member "scientific information extraction with semi supervised neural tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the stem ecr dataset grounding scientific entity references in stem scholarly content to authoritative encyclopedic and lexicographic sources")
  Member "multi task learning for speaker role adaptation in neural conversation models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "distant supervision for relation extraction beyond the sentence boundary":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "leveraging dependency forest for neural medical relation extraction")
  Member "lstm based conversation models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions")

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)