human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "analysing errors of open information extraction systems"
  Abstract: We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.

Connected through hyperedge HE-1 (induced by source paper "stress test evaluation for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a challenge set approach to evaluating machine translation"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "evaluating discourse phenomena in neural machine translation"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "neural open information extraction"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "open information extraction on scientific text an evaluation"):
  with members:
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"

Connected through hyperedge HE-4 (induced by source paper "a survey on open information extraction"):
  with members:
    - "graphene semantically linked propositions in open information extraction"
    - "getting more out of syntax with props"

Connected through hyperedge HE-5 (induced by source paper "wire57 a fine grained benchmark for open information extraction"):
  with members:
    - "getting more out of syntax with props"

Question: Please tell me which class the center node 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)