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: "holmes real time apt detection through correlation of suspicious information flows"
  Abstract: In this paper, we present HOLMES, a system that implements a new approach to the detection of Advanced and Persistent Threats (APTs). HOLMES is inspired by several case studies of real-world APTs that highlight some common goals of APT actors. In a nutshell, HOLMES aims to produce a detection signal that indicates the presence of a coordinated set of activities that are part of an APT campaign. One of the main challenges addressed by our approach involves developing a suite of techniques that make the detection signal robust and reliable. At a high-level, the techniques we develop effectively leverage the correlation between suspicious information flows that arise during an attacker campaign. In addition to its detection capability, HOLMES is also able to generate a high-level graph that summarizes the attacker's actions in real-time. This graph can be used by an analyst for an effective cyber response. An evaluation of our approach against some real-world APTs indicates that HOLMES can detect APT campaigns with high precision and low false alarm rate. The compact high-level graphs produced by HOLMES effectively summarizes an ongoing attack campaign and can assist real-time cyber-response operations.

Connected through hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"

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"
    - "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"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-3 (induced by source paper "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"):
  with members:
    - "tiresias predicting security events through deep learning"
    - "dynamic word embeddings for evolving semantic discovery"
    - "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "tweet2vec character based distributed representations for social media"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "propatrol attack investigation via extracted high level tasks"):
  with members:
    - "sleuth real time attack scenario reconstruction from cots audit data"

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)