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 "discovering archetypes to interpret evolution of individual behavior"):
  Abstract: In this paper, we aim to discover archetypical patterns of individual evolution in large social networks. In our work, an archetype comprises of \emph{progressive stages} of distinct behavior. We introduce a novel Gaussian Hidden Markov Model (G-HMM) Cluster to identify archetypes of evolutionary patterns. G-HMMs allow for: near limitless behavioral variation; imposing constraints on how individuals can evolve; different evolutionary rates; and are parsimonious. Our experiments with Academic and StackExchange dataset discover insightful archetypes. We identify four archetypes for researchers : \emph{Steady}, \emph{Diverse}, \emph{Evolving} and \emph{Diffuse}. We observe clear differences in the evolution of male and female researchers within the same archetype. Specifically, women and men differ within an archetype (e.g. \emph{Diverse}) in how they start, how they transition and the time spent in mid-career. We also found that the differences in grant income are better explained by the differences in archetype than by differences in gender. For StackOverflow, discovered archetypes could be labeled as: \emph{Experts}, \emph{Seekers}, \emph{Enthusiasts} and \emph{Facilitators}. We have strong quantitative results with competing baselines for activity prediction and perplexity. For future session prediction, the proposed G-HMM cluster model improves by an average of 32\% for different Stack Exchanges and 24\% for Academic dataset. Our model also exhibits lower perplexity than the baselines.

Includes members:
  Member "can we count on social media metrics first insights into the active scholarly use of social media":
    also connected through other hyperedges:
      (none)
  Member "career transitions and trajectories a case study in computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automated discovery and classification of training videos for career progression")
  Member "the misleading narrative of the canonical faculty productivity trajectory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "historical comparison of gender inequality in scientific careers across countries and disciplines")
  Member "gender productivity and prestige in computer science faculty hiring networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pipeline interventions")
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal probability calibration")
  Member "clustering hidden markov models with variational hem":
    also connected through other hyperedges:
      (none)

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.SI(Social and Information Networks)