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: "improved latency communication trade off for map shuffle reduce systems with stragglers"
  Abstract: In a distributed computing system operating according to the map-shuffle-reduce framework, coding data prior to storage can be useful both to reduce the latency caused by straggling servers and to decrease the inter-server communication load in the shuffling phase. In prior work, a concatenated coding scheme was proposed for a matrix multiplication task. In this scheme, the outer Maximum Distance Separable (MDS) code is leveraged to correct erasures caused by stragglers, while the inner repetition code is used to improve the communication efficiency in the shuffling phase by means of coded multicasting. In this work, it is demonstrated that it is possible to leverage the redundancy created by repetition coding in order to increase the rate of the outer MDS code and hence to increase the multicasting opportunities in the shuffling phase. As a result, the proposed approach is shown to improve over the best known latency-communication overhead trade-off.

Connected through hyperedge HE-1 (induced by source paper "a fundamental storage communication tradeoff in distributed computing with straggling nodes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"

Connected through hyperedge HE-2 (induced by source paper "coded federated computing in wireless networks with straggling devices and imperfect csi"):
  with members:
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "a storage computation communication tradeoff for distributed computing"
    - "wireless data center networks advances challenges and opportunities"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "wireless mapreduce distributed computing"
    - "wireless map reduce distributed computing with full duplex radios and imperfect csi"

Connected through hyperedge HE-3 (induced by source paper "on model coding for distributed inference and transmission in mobile edge computing systems"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "distributed gradient descent with coded partial gradient computations"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "coding for distributed fog computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"

Connected through hyperedge HE-4 (induced by source paper "wireless map reduce distributed computing with full duplex radios and imperfect csi"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "data shuffling in wireless distributed computing via low rank optimization"
    - "wireless mapreduce distributed computing"
    - "cloud edge non orthogonal transmission for fog networks with delayed csi at the cloud"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"

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)