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 "on the fly detection of autogenerated tweets"):
  Abstract: Most previous work related to tweet classification have focused on identifying a given tweet as a spam, or to classify a Twitter user account as a spammer or a bot. In most cases the tweet classification has taken place offline, on a pre-collected dataset of tweets. In this paper we present an \emph{on-the-fly} approach to classify each newly downloaded tweet as \emph{autogenerated} or not. We define an autogenerated tweet (AGT) as a tweet where all or parts of the natural language content is generated automatically by a bot or other type of program. #R##N#Our on-the-fly approach makes use of two classifiers. The first classifies a tweet solely based on the twitter text and the tweet metadata that comes with every tweet. It is used for tweets posted by unknown users with no available tweet history. An unknown user also triggers a batch job to start downloading the missing user timeline information. The second classifier is used for tweets posted by a user where the user timeline is downloaded and available. Initially, it will be the first classifier that handles most of the tweets. This will gradually change and after an initialization phase where we download historic data for the most active users, we reach a state where the second classifier handles a vast majority of all the tweets. #R##N#A simulation using our on-the-fly detection mechanism indicates that we can handle Twitter streams with up to 68,000 unique users each day. The bottleneck is the time required to download new user timelines. The AGT detection is very accurate. In a set of 5,000 tweets we correctly classified about 98\% of all AGTs using a subject-wise cross-validation.

Includes members:
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "the darpa twitter bot challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "bots strongerin and brexit computational propaganda during the uk eu referendum":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how many bots are you following")
  Member "making the most of tweet inherent features for social spam detection on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time detection of content polluters in partially observable twitter networks")
  Member "where in the world are you geolocation and language identification in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "estimating continuous distributions in bayesian classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "needmining identifying micro blog data containing customer needs")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mining social media data for biomedical signals and health related behavior")

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)