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: "modeling and analysis of abnormality detection in biomolecular nano networks"
  Abstract: a b s t r a c t A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured by variations in parameters of a nano-communications channel. In the second step, the SNMs transmit micro-scale messages over a noisy micro communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. The detection performance of each SNM is analyzed by setting up a Neyman-Pearson test. Next, taking into account the effect of the MCC, the overall performance of the proposed NADS is quantified in terms of probabilities of misdetection and false alarm. A design problem is formulated, when the optimized concentration of SNMs in a sample is obtained for a high probability of detection and a limited probability of false alarm.

Connected through hyperedge HE-1 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines"):
  with members:
    - "on flow induced diffusive mobile molecular communication first hitting time and performance analysis"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "advanced target detection via molecular communication"
    - "cooperative abnormality detection via diffusive molecular communications"
    - "diffusive mobile molecular communications over time variant channels"
    - "modeling duct flow for molecular communication"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "channel estimation for diffusive molecular communications"

Connected through hyperedge HE-2 (induced by source paper "advanced target detection via molecular communication"):
  with members:
    - "cooperative abnormality detection via diffusive molecular communications"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"

Connected through hyperedge HE-3 (induced by source paper "cooperative abnormality detection via diffusive molecular communications"):
  with members:
    - "symbol synchronization for diffusive molecular communication systems"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "channel estimation for diffusive molecular communications"
    - "distributed cooperative detection for multi receiver molecular communication"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "diffusion based nanonetworking a new modulation technique and performance analysis"

Connected through hyperedge HE-4 (induced by source paper "abnormality detection in correlated gaussian molecular nano networks design and analysis"):
  with members:
    - "information rates of ask based molecular communication in fluid media"
    - "arrival modeling and error analysis for molecular communication via diffusion with drift"
    - "improving receiver performance of diffusive molecular communication with enzymes"

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.IT(Information Theory)