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 "better safe than sorry an adversarial approach to improve social bot detection"):
  Abstract: The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.

Includes members:
  Member "social fingerprinting detection of spambot groups through dna inspired behavioral modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "charting the landscape of online cryptocurrency manipulation")
  Member "spectrum based deep neural networks for fraud detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fraud detection in networks state of the art")
  Member "hardening classifiers against evasion the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on training robust pdf malware classifiers")
  Member "holoscope topology and spike aware fraud detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fraud detection in networks state of the art")
  Member "the paradigm shift of social spambots evidence theories and tools for the arms race":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how many bots are you following")
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "botivist calling volunteers to action using online bots":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how many bots are you following")
  Member "dna inspired online behavioral modeling and its application to spambot detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uncovering coordinated networks on social media")

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)