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: "on distributed storage allocations of large files for maximum service rate"
  Abstract: Allocation of (redundant) file chunks throughout a distributed storage system affects important performance metrics such as the probability of file recovery, data download time, or the service rate of the system under a given data access model. This paper is concerned with the service rate under the assumption that the stored data is large and its download time is not negligible. We focus on quasi-uniform storage allocations and provide a service rate analysis for two common data access models. We find that the optimal allocation varies in accordance with different system parameters. This was not the case under the assumption that the download time does not scale with the size of data, where the minimal spreading allocation was previously found to be universally optimal.

Connected through hyperedge HE-1 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes"):
  with members:
    - "a geometric view of the service rates of codes problem and its application to the service rate of the first order reed muller codes"
    - "load balancing performance in distributed storage with regular balanced redundancy"
    - "analyzing the download time of availability codes"
    - "service rate region of content access from erasure coded storage"
    - "simplex queues for hot data download"
    - "batch and pir codes and their connections to locally repairable codes"
    - "on the service capacity region of accessing erasure coded content"
    - "on storage allocation for maximum service rate in distributed storage systems"

Connected through hyperedge HE-2 (induced by source paper "a geometric view of the service rates of codes problem and its application to the service rate of the first order reed muller codes"):
  with members:
    - "load balancing performance in distributed storage with regular balanced redundancy"
    - "service rate region of content access from erasure coded storage"
    - "simplex queues for hot data download"
    - "on the service capacity region of accessing erasure coded content"
    - "on storage allocation for maximum service rate in distributed storage systems"
    - "many to many matching games for proactive social caching in wireless small cell networks"
    - "distributed storage allocations"
    - "on the locality of codeword symbols"

Connected through hyperedge HE-3 (induced by source paper "private and rateless adaptive coded matrix vector multiplication"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "analyzing the download time of availability codes"
    - "secure coded cooperative computation at the heterogeneous edge against byzantine attacks"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "gasp codes for secure distributed matrix multiplication"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

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.DC(Distributed, Parallel, and Cluster Computing)