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: "crime topic modeling"
  Abstract: The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime type labels. This information loss impacts our ability to not only understand the causes of crime, but also how to develop optimal crime prevention strategies. We apply machine learning methods to short narrative text descriptions accompanying crime records with the goal of discovering ecologically more meaningful latent crime classes. We term these latent classes ‘crime topics’ in reference to text-based topic modeling methods that produce them. We use topic distributions to measure clustering among formally recognized crime types. Crime topics replicate broad distinctions between violent and property crime, but also reveal nuances linked to target characteristics, situational conditions and the tools and methods of attack. Formal crime types are not discrete in topic space. Rather, crime types are distributed across a range of crime topics. Similarly, individual crime topics are distributed across a range of formal crime types. Key ecological groups include identity theft, shoplifting, burglary and theft, car crimes and vandalism, criminal threats and confidence crimes, and violent crimes. Though not a replacement for formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime.

Connected through hyperedge HE-1 (induced by source paper "prediction focused topic models via feature selection"):
  with members:
    - "prior matters simple and general methods for evaluating and improving topic quality in topic modeling"
    - "prediction constrained topic models for antidepressant recommendation"
    - "variable selection for latent dirichlet allocation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-2 (induced by source paper "prediction focused topic models for electronic health records"):
  with members:
    - "prior matters simple and general methods for evaluating and improving topic quality in topic modeling"
    - "prediction constrained topic models for antidepressant recommendation"
    - "end to end learning of lda by mirror descent back propagation over a deep architecture"
    - "variable selection for latent dirichlet allocation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-3 (induced by source paper "prediction focused topic models via vocab selection"):
  with members:
    - "prediction constrained topic models for antidepressant recommendation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-4 (induced by source paper "women in isis propaganda a natural language processing analysis of topics and emotions in a comparison with mainstream religious group"):
  with members:
    - "text classification algorithms a survey"

Connected through hyperedge HE-5 (induced by source paper "multivariate spatiotemporal hawkes processes and network reconstruction"):
  with members:
    - "inferring multiplex diffusion network via multivariate marked hawkes process"
    - "graph based deep modeling and real time forecasting of sparse spatio temporal data"
    - "the role of network analysis in industrial and applied mathematics a physical applied mathematics perspective"
    - "latent self exciting point process model for spatial temporal networks"
    - "the geospatial characteristics of a social movement communication network"
    - "modeling events with cascades of poisson processes"
    - "the anatomy of the facebook social graph"

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)