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: "demand aware network designs of bounded degree"
  Abstract: Traditionally, networks such as datacenter interconnects are designed to optimize worst-case performance under arbitrary traffic patterns. Such network designs can however be far from optimal when considering the actual workloads and traffic patterns which they serve. This insight led to the development of demand-aware datacenter interconnects which can be reconfigured depending on the workload. #R##N#Motivated by these trends, this paper initiates the algorithmic study of demand-aware networks (DANs) designs, and in particular the design of bounded-degree networks. The inputs to the network design problem are a discrete communication request distribution, D, defined over communicating pairs from the node set V , and a bound, d, on the maximum degree. In turn, our objective is to design an (undirected) demand-aware network N = (V,E) of bounded-degree d, which provides short routing paths between frequently communicating nodes distributed across N. In particular, the designed network should minimize the expected path length on N (with respect to D), which is a basic measure of the efficiency of the network. #R##N#We show that this fundamental network design problem exhibits interesting connections to several classic combinatorial problems and to information theory. We derive a general lower bound based on the entropy of the communication pattern D, and present asymptotically optimal network-aware design algorithms for important distribution families, such as sparse distributions and distributions of locally bounded doubling dimensions.

Connected through hyperedge HE-1 (induced by source paper "scheduling for weighted flow and completion times in reconfigurable networks"):
  with members:
    - "toward demand aware networking a theory for self adjusting networks"

Connected through hyperedge HE-2 (induced by source paper "renets toward statically optimal self adjusting networks"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "toward demand aware networking a theory for self adjusting networks"
    - "online balanced repartitioning"
    - "high throughput data center topology design"
    - "jellyfish networking data centers randomly"
    - "competitive paging algorithms"

Connected through hyperedge HE-3 (induced by source paper "self adjusting linear networks"):
  with members:
    - "toward demand aware networking a theory for self adjusting networks"
    - "towards communication aware robust topologies"
    - "locally self adjusting skip graphs"
    - "online balanced repartitioning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "toward demand aware networking a theory for self adjusting networks"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "virtual network embedding approximations leveraging randomized rounding"
    - "repetita repeatable experiments for performance evaluation of traffic engineering algorithms"
    - "concurrent self adjusting distributed tree networks"
    - "online balanced repartitioning"

Connected through hyperedge HE-5 (induced by source paper "push down trees optimal self adjusting complete trees"):
  with members:
    - "the landscape of bounds for binary search trees"
    - "an o loglog n competitive binary search tree with optimal worst case access times"

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)