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: "data centric misbehavior detection in vanets"
  Abstract: Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.

Connected through hyperedge HE-1 (induced by source paper "scaling pseudonymous authentication for large mobile systems"):
  with members:
    - "efficient scalable and resilient vehicle centric certificate revocation list distribution in vanets"
    - "blockpki an automated resilient and transparent public key infrastructure"
    - "a security credential management system for v2x communications"
    - "vpkiaas a highly available and dynamically scalable vehicular public key infrastructure"
    - "rhythm a randomized hybrid scheme to hide in the mobile crowd"
    - "secmace scalable and robust identity and credential management infrastructure in vehicular communication systems"
    - "security and privacy in vehicular social networks"
    - "evaluating on demand pseudonym acquisition policies in vehicular communication systems"

Connected through hyperedge HE-2 (induced by source paper "survey on misbehavior detection in cooperative intelligent transportation systems"):
  with members:
    - "a security credential management system for v2x communications"
    - "veremi a dataset for comparable evaluation of misbehavior detection in vanets"
    - "analyzing attacks on cooperative adaptive cruise control cacc"
    - "secmace scalable and robust identity and credential management infrastructure in vehicular communication systems"
    - "survey of important issues in uav communication networks"
    - "enhanced position verification for vanets using subjective logic"
    - "secure vehicular communication systems implementation performance and research challenges"
    - "secure vehicular communication systems design and architecture"

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)