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: "exploration and exploitation of victorian science in darwin s reading notebooks"
  Abstract: Abstract   Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin’s behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin’s own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin’s consumption more exploratory than the culture’s production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

Connected through hyperedge HE-1 (induced by source paper "how we do things with words analyzing text as social and cultural data"):
  with members:
    - "a kernel independence test for geographical language variation"
    - "why should i trust you explaining the predictions of any classifier"
    - "predicting relevance based on assessor disagreement analysis and practical applications for search evaluation"
    - "empath understanding topic signals in large scale text"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "computational sociolinguistics a survey"
    - "gender identity and lexical variation in social media"

Connected through hyperedge HE-2 (induced by source paper "topic modeling the reading and writing behavior of information foragers"):
  with members:
    - "multi level computational methods for interdisciplinary research in the hathitrust digital library"
    - "towards evaluation of cultural scale claims in light of topic model sampling effects"
    - "the wisdom of the few supertaggers in collaborative tagging systems"
    - "accuracy of simple initials based methods for author name disambiguation"
    - "multilingual topic models for unaligned text"
    - "a family of statistical symmetric divergences based on jensen s inequality"

Connected through hyperedge HE-3 (induced by source paper "levy flights of the collective imagination"):
  with members:
    - "the development of darwin s origin of species"

Connected through hyperedge HE-4 (induced by source paper "multi level computational methods for interdisciplinary research in the hathitrust digital library"):
  with members:
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "rank diversity of languages generic behavior in computational linguistics"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

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)