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: "beyond google play a large scale comparative study of chinese android app markets"
  Abstract: China is one of the largest Android markets in the world. As Chinese users cannot access Google Play to buy and install Android apps, a number of independent app stores have emerged and compete in the Chinese app market. Some of the Chinese app stores are pre-installed vendor-specific app markets (e.g., Huawei, Xiaomi and OPPO), whereas others are maintained by large tech companies (e.g., Baidu, Qihoo 360 and Tencent). The nature of these app stores and the content available through them vary greatly, including their trustworthiness and security guarantees. #R##N#As of today, the research community has not studied the Chinese Android ecosystem in depth. To fill this gap, we present the first large-scale comparative study that covers more than 6 million Android apps downloaded from 16 Chinese app markets and Google Play. We focus our study on catalog similarity across app stores, their features, publishing dynamics, and the prevalence of various forms of misbehavior (including the presence of fake, cloned and malicious apps). Our findings also suggest heterogeneous developer behavior across app stores, in terms of code maintenance, use of third-party services, and so forth. Overall, Chinese app markets perform substantially worse when taking active measures to protect mobile users and legit developers from deceptive and abusive actors, showing a significantly higher prevalence of malware, fake, and cloned apps than Google Play.

Connected through hyperedge HE-1 (induced by source paper "characterizing cryptocurrency exchange scams"):
  with members:
    - "characterizing eosio blockchain"
    - "large scale analysis of pop up scam on typosquatting urls"
    - "a large scale empirical study on industrial fake apps"
    - "data mining for detecting bitcoin ponzi schemes"
    - "rebooting research on detecting repackaged android apps literature review and benchmark"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "an analysis of attacks on blockchain consensus"

Connected through hyperedge HE-2 (induced by source paper "maddroid characterising and detecting devious ad content for android apps"):
  with members:
    - "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"
    - "yolov3 an incremental improvement"
    - "dating with scambots understanding the ecosystem of fraudulent dating applications"
    - "androzoo collecting millions of android apps and their metadata for the research community"
    - "frauddroid automated ad fraud detection for android apps"
    - "an investigation into the use of common libraries in android apps"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "devicewatch identifying compromised mobile devices through network traffic analysis and graph inference"):
  with members:
    - "characterizing location based mobile tracking in mobile ad networks"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "node2vec scalable feature learning for networks"
    - "personalized security indicators to detect application phishing attacks in mobile platforms"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-4 (induced by source paper "uncovering download fraud activities in mobile app markets"):
  with members:
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "fairplay fraud and malware detection in google play"
    - "xgboost a scalable tree boosting system"
    - "the dark side of micro task marketplaces characterizing fiverr and automatically detecting crowdturfing"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-5 (induced by source paper "don t pick the cherry an evaluation methodology for android malware detection methods"):
  with members:
    - "android hiv a study of repackaging malware for evading machine learning detection"
    - "rebooting research on detecting repackaged android apps literature review and benchmark"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "mamadroid detecting android malware by building markov chains of behavioral models"

Connected through hyperedge HE-6 (induced by source paper "an analysis of pre installed android software"):
  with members:
    - "an investigation into the use of common libraries in android apps"
    - "haystack a multi purpose mobile vantage point in user space"

Connected through hyperedge HE-7 (induced by source paper "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"):
  with members:
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "a large scale investigation of obfuscation use in google play"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "reviewer integration and performance measurement for malware detection"
    - "an investigation into the use of common libraries in android apps"

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.NI(Networking and Internet Architecture)