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: "datacenter traffic control understanding techniques and tradeoffs"
  Abstract: Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today’s cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control, including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters, which have been receiving increasing attention recently and pose interesting and novel research problems. To measure the performance of datacenter networks, different performance metrics have been used, such as flow completion times, deadline miss rate, throughput, and fairness. Depending on the application and user requirements, some metrics may need more attention. While investigating different traffic control techniques, we point out the tradeoffs involved in terms of costs, complexity, and performance. We find that a combination of different traffic control techniques may be necessary at particular entities and layers in the network to improve the variety of performance metrics. We also find that despite significant research efforts, there are still open problems that demand further attention from the research community.

Connected through hyperedge HE-1 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks"):
  with members:
    - "fog assisted caching employing solar renewable energy for delivering video on demand service"
    - "energy efficient and resilient infrastructure for fog computing health monitoring applications"
    - "virtual machines embedding for cloud pon awgr and server based data centres"
    - "a survey on geographically distributed big data processing using mapreduce"
    - "disaggregation for improved efficiency in fog computing era"
    - "experimental evaluation of server centric passive optical network based data centre architecture"
    - "energy efficient distributed processing in vehicular cloud architecture"
    - "energy efficient software matching in vehicular fog"

Connected through hyperedge HE-2 (induced by source paper "a survey on recent advances in transport layer protocols"):
  with members:
    - "a quic implementation for ns 3"
    - "beyond socket options making the linux tcp stack truly extensible"
    - "low delay scheduling of objects over multiple wireless paths"
    - "elasticity detection a building block for delay sensitive congestion control"
    - "qaware a cross layer approach to mptcp scheduling"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "machine learning for networking workflow advances and opportunities"
    - "mptcp meets fec supporting latency sensitive applications over heterogeneous networks"

Connected through hyperedge HE-3 (induced by source paper "renets toward statically optimal self adjusting networks"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "toward demand aware networking a theory for self adjusting networks"
    - "demand aware network designs of bounded degree"
    - "online balanced repartitioning"
    - "high throughput data center topology design"
    - "jellyfish networking data centers randomly"
    - "competitive paging algorithms"

Connected through hyperedge HE-4 (induced by source paper "efficient distributed workload re embedding"):
  with members:
    - "toward demand aware networking a theory for self adjusting networks"
    - "online balanced repartitioning"
    - "dynamic balanced graph partitioning"
    - "online companion caching"
    - "competitive paging algorithms"

Connected through hyperedge HE-5 (induced by source paper "toward demand aware networking a theory for self adjusting networks"):
  with members:
    - "virtual network embedding approximations leveraging randomized rounding"
    - "repetita repeatable experiments for performance evaluation of traffic engineering algorithms"
    - "demand aware network designs of bounded degree"
    - "concurrent self adjusting distributed tree networks"
    - "online balanced repartitioning"

Connected through hyperedge HE-6 (induced by source paper "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"):
  with members:
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "near data scheduling for data centers with multiple levels of data locality"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "multicast traffic engineering for software defined networks"

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)