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: "graph drawing by stochastic gradient descent"
  Abstract: A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs.

Connected through hyperedge HE-1 (induced by source paper "batchlayout a batch parallel force directed graph layout algorithm in shared memory"):
  with members:
    - "multi level tree based approach for interactive graph visualization with semantic zoom"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a distributed multilevel force directed algorithm"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-2 (induced by source paper "resilience by reconfiguration exploiting heterogeneity in robot teams"):
  with members:
    - "robustness of information diffusion algorithms to locally bounded adversaries"
    - "circle packing for origami design is hard"

Connected through hyperedge HE-3 (induced by source paper "further towards unambiguous edge bundling investigating power confluent drawings for network visualization"):
  with members:
    - "strict confluent drawing"
    - "improved optimal and approximate power graph compression for clearer visualisation of dense graphs"
    - "confluent layered drawings"
    - "delta confluent drawings"
    - "confluent drawings visualizing non planar diagrams in a planar way"

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.CG(Computational Geometry)