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: "compressed coded distributed computing"
  Abstract: Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme.

Connected through hyperedge HE-1 (induced by source paper "coded distributed computing schemes with smaller numbers of input files and output functions"):
  with members:
    - "resolvable designs for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on the placement delivery array design for centralized coded caching scheme"

Connected through hyperedge HE-2 (induced by source paper "resolvable designs for speeding up distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "coded computing for distributed graph analytics"
    - "a new combinatorial design of coded distributed computing"
    - "communication vs distributed computation an alternative trade off curve"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "the benefit of being flexible in distributed computation"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "camr coded aggregated mapreduce"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "coded computing for distributed graph analytics"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "a new combinatorial design of coded distributed computing"

Connected through hyperedge HE-5 (induced by source paper "coded computing for distributed graph analytics"):
  with members:
    - "coded cooperative computation for internet of things"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "fundamental limits of coded linear transform"
    - "fundamental resource trade offs for encoded distributed optimization"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "near optimal coded data shuffling for distributed learning"

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)