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: "detection of early stage enterprise infection by mining large scale log data"
  Abstract: Recent years have seen the rise of more sophisticated attacks including advanced persistent threats (APTs) which pose severe risks to organizations and governments by targeting confidential proprietary information. Additionally, new malware strains are appearing at a higher rate than ever before. Since many of these malware are designed to evade existing security products, traditional defenses deployed by most enterprises today, e.g., anti-virus, firewalls, intrusion detection systems, often fail at detecting infections at an early stage. #R##N#We address the problem of detecting early-stage infection in an enterprise setting by proposing a new framework based on belief propagation inspired from graph theory. Belief propagation can be used either with "seeds" of compromised hosts or malicious domains (provided by the enterprise security operation center -- SOC) or without any seeds. In the latter case we develop a detector of C&C communication particularly tailored to enterprises which can detect a stealthy compromise of only a single host communicating with the C&C server. #R##N#We demonstrate that our techniques perform well on detecting enterprise infections. We achieve high accuracy with low false detection and false negative rates on two months of anonymized DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of real-world web proxy logs collected at the border of a large enterprise. Through careful manual investigation in collaboration with the enterprise SOC, we show that our techniques identified hundreds of malicious domains overlooked by state-of-the-art security products.

Connected through hyperedge HE-1 (induced by source paper "attacking graph based classification via manipulating the graph structure"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "structure based sybil detection in social networks via local rule based propagation"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-2 (induced by source paper "poirot aligning attack behavior with kernel audit records for cyber threat hunting"):
  with members:
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "propatrol attack investigation via extracted high level tasks"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "aiql enabling efficient attack investigation from system monitoring data"
    - "behavior query discovery in system generated temporal graphs"
    - "efficient subgraph matching on billion node graphs"

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"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "logzip extracting hidden structures via iterative clustering for log compression"):
  with members:
    - "a directed acyclic graph approach to online log parsing"
    - "tools and benchmarks for automated log parsing"
    - "the unified logging infrastructure for data analytics at twitter"

Connected through hyperedge HE-5 (induced by source paper "contemporary software monitoring a systematic literature review"):
  with members:
    - "big data meets hpc log analytics scalable approach to understanding systems at extreme scale"
    - "a big data analyzer for large trace logs"

Connected through hyperedge HE-6 (induced by source paper "d iot a federated self learning anomaly detection system for iot"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "federated learning strategies for improving communication efficiency"
    - "dolphinattack inaudible voice commands"
    - "robust physical world attacks on machine learning models"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "graph based security and privacy analytics via collective classification with joint weight learning and propagation"):
  with members:
    - "structure based sybil detection in social networks via local rule based propagation"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "node2vec scalable feature learning for networks"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "deepwalk online learning of social representations"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "linearized and single pass belief propagation"

Connected through hyperedge HE-8 (induced by source paper "iot keeper securing iot communications in edge networks"):
  with members:
    - "sequence aggregation rules for anomaly detection in computer network traffic"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "one class collective anomaly detection based on long short term memory recurrent neural networks"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "adversarial learning a critical review and active learning study"
    - "collective anomaly detection based on long short term memory recurrent neural network"

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.CR(Cryptography and Security)