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: "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
  Abstract: Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a position-aided approach where the vehicle's position (e.g., available via GPS) is used to query the multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the larger beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.

Connected through hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks"):
  with members:
    - "adaptive millimeter wave communications exploiting mobility and blockage dynamics"
    - "second best beam alignment via bayesian multi armed bandits"
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "online learning for position aided millimeter wave beam training"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive millimeter wave communications exploiting mobility and blockage dynamics"):
  with members:
    - "mobility and blockage aware communications in millimeter wave vehicular networks"
    - "second best beam alignment via bayesian multi armed bandits"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "online learning for position aided millimeter wave beam training"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"

Connected through hyperedge HE-3 (induced by source paper "toward intelligent vehicular networks a machine learning framework"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "machine learning for vehicular networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "vehicular communications a network layer perspective"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-4 (induced by source paper "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "deep learning for massive mimo csi feedback"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "channel fingerprint based beam tracking for millimeter wave communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-6 (induced by source paper "physical layer identification based on spatial temporal beam features for millimeter wave wireless networks"):
  with members:
    - "ieee 802 11ad based radar an approach to joint vehicular communication radar system"
    - "a curriculum learning method for improved noise robustness in automatic speech recognition"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"

Connected through hyperedge HE-7 (induced by source paper "deep learning coordinated beamforming for highly mobile millimeter wave systems"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "base station diversity propagation measurements at 73 ghz millimeter wave for 5g coordinated multipoint comp analysis"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "millimeter wave location based beamforming using compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "online learning for position aided millimeter wave beam training"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "robust location aided beam alignment in millimeter wave massive mimo"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

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.IT(Information Theory)