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 "surface semantically rich fact validation with explanations"):
  Abstract: Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant evidential sentences in the trusted source and (ii) use them to classify the input sentence's veracity. An evaluation of our efficient three-parameter model on the FEVER dataset showed an improvement of 90% over the state-of-the-art baseline on retrieving relevant sentences and a 70% relative improvement in classification.

Includes members:
  Member "all in one multi task learning for rumour verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "kg cleaner identifying and correcting errors produced by information extraction systems":
    also connected through other hyperedges:
      (none)
  Member "fever a large scale dataset for fact extraction and verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "a simple but tough to beat baseline for the fake news challenge stance detection task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards quantifying the distance between opinions")
  Member "automatic detection of fake news":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hgat hierarchical graph attention network for fake news detection")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "probabilistic similarity logic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non parametric learning of gaifman models")

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)