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: "investigating evaluation of open domain dialogue systems with human generated multiple references"
  Abstract: The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog. One alternative is to collect human annotations for evaluation, which can be expensive and time consuming. To demonstrate the effectiveness of multi-reference evaluation, we augment the test set of DailyDialog with multiple references. A series of experiments show that the use of multiple references results in improved correlation between several automatic metrics and human judgement for both the quality and the diversity of system output.

Connected through hyperedge HE-1 (induced by source paper "detecting asks in se attacks impact of linguistic and structural knowledge"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "allennlp a deep semantic natural language processing platform"
    - "neural approaches to conversational ai"
    - "augmenting neural response generation with context aware topical attention"

Connected through hyperedge HE-2 (induced by source paper "dialogpt large scale generative pre training for conversational response generation"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "dlgnet a transformer based model for dialogue response generation"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "consistent dialogue generation with self supervised feature learning"
    - "structuring latent spaces for stylized response generation"

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)