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: "panda extreme scale parallel k nearest neighbor on distributed architectures"
  Abstract: Computing k-Nearest Neighbors (KNN) is oneof the core kernels used in many machine learning, datamining and scientific computing applications. Although kd-treebased O(log n) algorithms have been proposed for computingKNN, due to its inherent sequentiality, linear algorithms arebeing used in practice. This limits the applicability of suchmethods to millions of data points, with limited scalabilityfor Big Data analytics challenges in the scientific domain. In this paper, we present parallel and highly optimized kdtreebased KNN algorithms (both construction and querying) suitable for distributed architectures. Our algorithm includesnovel approaches for pruning search space and improvingload balancing and partitioning among nodes and threads. Using TB-sized datasets from three science applications: astrophysics, plasma physics, and particle physics, we showthat our implementation can construct kd-tree of 189 billionparticles in 48 seconds on utilizing ~50,000 cores. We alsodemonstrate computation of KNN of 19 billion queries in12 seconds. We demonstrate almost linear speedup both forshared and distributed memory computers. Our algorithmsoutperforms earlier implementations by more than order ofmagnitude, thereby radically improving the applicability of ourimplementation to state-of-the-art Big Data analytics problems.

Connected through hyperedge HE-1 (induced by source paper "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"):
  with members:
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "coded fft and its communication overhead"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"

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)