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: "graphr accelerating graph processing using reram"
  Abstract: This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.

Connected through hyperedge HE-1 (induced by source paper "a survey on graph processing accelerators challenges and opportunities"):
  with members:
    - "flashabacus a self governing flash based accelerator for low power systems"
    - "relational inductive biases deep learning and graph networks"
    - "struc2vec learning node representations from structural identity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "democratizing design for future computing platforms"
    - "graph2vec learning distributed representations of graphs"
    - "novel graph processor architecture prototype system and results"
    - "5g green cellular networks considering power allocation schemes"

Connected through hyperedge HE-2 (induced by source paper "panther a programmable architecture for neural network training harnessing energy efficient reram"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "a study of bfloat16 for deep learning training"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "training and inference with integers in deep neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "training deep neural networks with 8 bit floating point numbers"
    - "revisiting small batch training for deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-3 (induced by source paper "smash co designing software compression and hardware accelerated indexing for efficient sparse matrix operations"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "sparse matrix multiplication on cam based accelerator"
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "snap a general purpose network analysis and graph mining library"
    - "sparse 3d convolutional neural networks"
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"

Connected through hyperedge HE-4 (induced by source paper "an overview of in memory processing with emerging non volatile memory for data intensive applications"):
  with members:
    - "red a reram based deconvolution accelerator"
    - "mixed precision in memory computing"
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "temporal correlation detection using computational phase change memory"
    - "computing in memory with spin transfer torque magnetic ram"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "storage class memory principles problems and possibilities"):
  with members:
    - "enabling fine grain restricted coset coding through word level compression for pcm"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-6 (induced by source paper "chonda near data acceleration with concurrent host access"):
  with members:
    - "drama exploiting dram addressing for cross cpu attacks"

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)