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 "towards better understanding of spontaneous conversations overcoming automatic speech recognition errors with intent recognition"):
  Abstract: In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework. Intent recognition is a powerful technique for dialog flow management in turn-oriented, human-machine dialogs. This technique can also be very useful in the context of human-human dialogs, though it serves a different purpose of key insight extraction from conversations. We argue that currently available intent recognition techniques are not applicable to human-human dialogs due to the complex structure of turn-taking and various disfluencies encountered in spontaneous conversations, exacerbated by speech recognition errors and scarcity of domain-specific labeled data. Without efficient key insight extraction techniques, raw human-human dialog transcripts remain significantly unexploited. #R##N#Our contribution consists of a novel FST for intent indexing and an algorithm for fuzzy intent search over the lattice - a compact graph encoding of ASR's hypotheses. We also develop a pruning strategy to constrain the fuzziness of the FST index search. Extracted intents represent linguistic domain knowledge and help us improve (rescore) the original transcript. We compare our method with a baseline, which uses only the most likely transcript hypothesis (best path), and find an increase in the total number of recognized intents by 25%.

Includes members:
  Member "composing finite state transducers on gpus":
    also connected through other hyperedges:
      (none)
  Member "the capio 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "punctuation prediction model for conversational speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust neural machine translation for clean and noisy speech transcripts")
  Member "the marchex 2018 english conversational telephone speech recognition system":
    also connected through other hyperedges:
      (none)
  Member "just ask building an architecture for extensible self service spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "the microsoft 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep segmental phonetic posterior grams based discovery of non categories in l2 english speech")
  Member "encodingword confusion networks with recurrent neural networks for dialog state tracking":
    also connected through other hyperedges:
      (none)
  Member "finding consensus in speech recognition word error minimization and other applications of confusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling asr ambiguity for dialogue state tracking using word confusion networks")

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)