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: "on unified vehicular communications and radar sensing in millimeter wave and low terahertz bands"
  Abstract: Future smart vehicles will incorporate high-data-rate communications and high-resolution radar sensing capabilities operating in the millimeter-wave and higher frequencies. These two systems are preparing to share and reuse a lot of common functionalities, such as steerable millimeter-wave antenna arrays. Motivated by this growing overlap, and advanced further by the space and cost constraints, the vehicular community is pursuing a vision of unified vehicular communications and radar sensing, which represents a major paradigm shift for next-generation connected and self-driving cars. This article outlines a path to materialize this decisive transformation. We begin by reviewing the latest developments in hybrid vehicular communications and radar systems, and then propose a concept of unified channel access over millimeter-wave and higher frequencies. Our supporting system-level performance characterization relies upon real-life measurements and massive ray-based modeling to confirm the significant improvements brought by our proposal to mitigating the interference and deafness effects. Since our results aim to open the door to unified vehicular communications and radar sensing, we conclude by outlining the potential research directions in this rapidly developing field.

Connected through hyperedge HE-1 (induced by source paper "mac protocols for terahertz communication a comprehensive survey"):
  with members:
    - "on the reliability of wireless virtual reality at terahertz thz frequencies"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "survey on terahertz nanocommunication and networking a top down perspective"
    - "end to end simulation of 5g mmwave networks"
    - "wireless terahertz system architectures for networks beyond 5g"
    - "terahertz technologies to deliver optical network quality of experience in wireless systems beyond 5g"
    - "slotted csma ca based energy efficient mac protocol design in nanonetworks"
    - "last meter indoor terahertz wireless access performance insights and implementation roadmap"

Connected through hyperedge HE-2 (induced by source paper "semantic effectiveness filtering and control for post 5g wireless connectivity"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "federated optimization distributed machine learning for on device intelligence"
    - "device fingerprinting in wireless networks challenges and opportunities"

Connected through hyperedge HE-3 (induced by source paper "radchat spectrum sharing for automotive radar interference mitigation"):
  with members:
    - "joint design of overlaid communication systems and pulsed radars"
    - "survey of spectrum sharing for inter technology coexistence"
    - "millimeter wave vehicular communication to support massive automotive sensing"

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)