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: "dna inspired online behavioral modeling and its application to spambot detection"
  Abstract: We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks.

Connected through hyperedge HE-1 (induced by source paper "uncovering coordinated networks on social media"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "the spread of low credibility content by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-2 (induced by source paper "twitter bot detection using bidirectional long short term memory neural networks and word embeddings"):
  with members:
    - "on the use of bert for neural machine translation"
    - "dual fofe net neural models for entity linking with pagerank"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "arming the public with artificial intelligence to counter social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "deep neural networks for bot detection"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "deception strategies and threats for online discussions"
    - "the spread of low credibility content by social bots"
    - "a deep reinforcement learning chatbot"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"

Connected through hyperedge HE-4 (induced by source paper "the art of social bots a review and a refined taxonomy"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "false information on web and social media a survey"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "detecting influence campaigns in social networks using the ising model"
    - "botnet campaign detection on twitter"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-5 (induced by source paper "information pollution by social bots"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "the role of bot squads in the political propaganda on twitter"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "quantifying biases in online information exposure"
    - "the spread of low credibility content by social bots"
    - "how algorithmic popularity bias hinders or promotes quality"

Connected through hyperedge HE-6 (induced by source paper "scalable and generalizable social bot detection through data selection"):
  with members:
    - "real time event detection on social data streams"
    - "rtbust exploiting temporal patterns for botnet detection on twitter"
    - "its all in a name detecting and labeling bots by their name"
    - "arming the public with artificial intelligence to counter social bots"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "consistent individualized feature attribution for tree ensembles"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"

Connected through hyperedge HE-7 (induced by source paper "better safe than sorry an adversarial approach to improve social bot detection"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "spectrum based deep neural networks for fraud detection"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-8 (induced by source paper "rtbust exploiting temporal patterns for botnet detection on twitter"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "relational autoencoder for feature extraction"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

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)