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: "toward optimal performance with network assisted tcp at mobile edge"
  Abstract: In contrast to the classic fashion for designing distributed end-to-end (e2e) TCP schemes for cellular networks (CN), we explore another design space by having the CN assist the task of the transport control. We show that in the emerging cellular architectures such as mobile/multi-access edge computing (MEC), where the servers are located close to the radio access network (RAN), significant improvements can be achieved by leveraging the nature of the logically centralized network measurements at the RAN and passing information such as its minimum e2e delay and access link capacity to each server. Particularly, a Network Assistance module (located at the mobile edge) will pair up with wireless scheduler to provide feedback information to each server and facilitate the task of congestion control. To that end, we present two Network Assisted schemes called NATCP (a clean-slate design replacing TCP at end-hosts) and NACubic (a backward compatible design requiring no change for TCP at end-hosts). Our preliminary evaluations using real cellular traces show that both schemes dramatically outperform existing schemes both in single-flow and multi-flow scenarios.

Connected through hyperedge HE-1 (induced by source paper "to schedule or not to schedule when no scheduling can beat the best known flow scheduling algorithm in datacenter networks"):
  with members:
    - "sharpedge an asynchronous and core agnostic solution to guarantee bounded delays"
    - "make tcp great again in cellular networks a deep reinforcement learning approach"
    - "hyline a simple and practical flow scheduling for commodity datacenters"
    - "cellular controlled delay tcp c2tcp"
    - "c2tcp a flexible cellular tcp to meet stringent delay requirements"
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-2 (induced by source paper "sharpedge an asynchronous and core agnostic solution to guarantee bounded delays"):
  with members:
    - "bounding queue delay in cellular networks to support ultra low latency applications"
    - "make tcp great again in cellular networks a deep reinforcement learning approach"
    - "hyline a simple and practical flow scheduling for commodity datacenters"
    - "cellular controlled delay tcp c2tcp"
    - "c2tcp a flexible cellular tcp to meet stringent delay requirements"
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-3 (induced by source paper "make tcp great again in cellular networks a deep reinforcement learning approach"):
  with members:
    - "bounding queue delay in cellular networks to support ultra low latency applications"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "cellular controlled delay tcp c2tcp"
    - "c2tcp a flexible cellular tcp to meet stringent delay requirements"
    - "successor features for transfer in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "bounding queue delay in cellular networks to support ultra low latency applications"):
  with members:
    - "cellular controlled delay tcp c2tcp"
    - "c2tcp a flexible cellular tcp to meet stringent delay requirements"
    - "bola near optimal bitrate adaptation for online videos"

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)