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: "you are no jack kennedy on media selection of highlights from presidential debates"
  Abstract: Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three-decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situations. We find that crowdworkers can only achieve an accuracy of 60% in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers’ free responses with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that “context matters”, whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage.

Connected through hyperedge HE-1 (induced by source paper "what gets echoed understanding the pointers in explanations of persuasive arguments"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "exploring the role of prior beliefs for argument persuasion"
    - "e snli natural language inference with natural language explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "opennmt open source toolkit for neural machine translation"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-2 (induced by source paper "no permanent friends or enemies tracking relationships between nations from news"):
  with members:
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "rookie a unique approach for exploring news archives"
    - "inferring interpersonal relations in narrative summaries"
    - "quotus the structure of political media coverage as revealed by quoting patterns"
    - "neural machine translation by jointly learning to align and translate"

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.SI(Social and Information Networks)